A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science
Open accessAbstract
Global climate change and its impact on human life has become one of our era's greatest challenges. Despite the urgency, data science has had little impact on furthering our understanding of our planet in spite of the abundance of climate data. This is a stark contrast from other fields such as advertising or electronic commerce where big data has been a great success story. This discrepancy stems from the complex nature of climate data as well as the scientific questions climate science brings forth. This article introduces a data science audience to the challenges and opportunities to mine large climate datasets, with an emphasis on the nuanced difference between mining climate data and traditional big data approaches. We focus on data, methods, and application challenges that must be addressed in order for big data to fulfill their promise with regard to climate science applications. More importantly, we highlight research showing that solely relying on traditional big data techniques results in dubious findings, and we instead propose a theory-guided data science paradigm that uses scientific theory to constrain both the big data techniques as well as the results-interpretation process to extract accurate insight from large climate data.
Verified 2026-10-09 · 2 references not checked yet
AI snapshot
Written from the paper’s full text.
TL;DR
This article argues that big data alone has had limited impact on climate science because climate data are spatiotemporally autocorrelated, heterogeneous, and theory-laden, unlike typical big data domains. It proposes theory-guided data science, in which scientific theory constrains both methods and interpretation, and illustrates this with climate examples.
Key findings
- Climate data violate traditional data science assumptions: they are auto- and cross-correlated in space and time, heterogeneous across sources, and affected by continuously changing observing systems; methods imposing independence assumptions have limited applicability.
- The paper cites a *Science* study linking sea surface temperatures to Amazon forest fires with a 0.9 correlation, but the fire record spanned only 10 years, so such high correlations could occur by random chance.
- In a relationship-mining example, 41,741 candidate vectors were tested as predictors of equilibrium climate sensitivity, but all potential relationships were not statistically significant once dependence between models, variables, locations, and seasons was accounted for.
- Google Flu Trends is used as a cautionary tale: a 2014 study found that for consecutive years Google made more bad predictions than good ones, attributed to overreliance on search queries and Google’s changing search algorithm.
Methods
- This is a conceptual/review article, not an empirical study; it synthesizes climate data challenges and examples from existing climate and data science research.
- No new dataset or sample is analyzed; cited examples include 242 tornado cases, 41,741 predictor vectors, and Google Flu Trends.
- The main analysis is a narrative synthesis of data, methods, and evaluation challenges, culminating in a proposal for theory-guided data science.
Limitations
- The paper does not state formal limitations of its own argument.
- Evident from the text: it is a perspective/review with selected examples rather than a systematic review or empirical test, and it provides no new quantitative validation of the proposed theory-guided paradigm.
- Evident from the text: the examples are drawn from other studies and may not cover all climate data or method challenges.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Cites
Statistics
Claims the paper makes
0 of 43 cited sources checked · 0 supported · 0 flagged
- Interpretationreasoning
Any methods that impose independence assumptions among data points will have limited practicality with climate data.
“Hence, any methods that impose independence assumptions among data points will have limited practicality with such data.”
Quote in the paperSubstantial claim- Rests on an assumption: Climate data are auto-correlated where regions in spatial or temporal proximity tend to be highly related.
- Interpretationreasoning
Changes in instruments and data processing algorithms put into question the applicability of such data to study long-term climate.
“Changes in instruments and data processing algorithms put into question the applicability of such data to study long-term climate.”
Quote in the paperSubstantial claim - Premisecitation
The field of data science has historically focused on certain tasks and evaluation metrics that are not applicable to some of climate science's biggest needs.
“Second, the field of data science has historically focused on certain tasks and evaluation metrics 2 that are not applicable to some of climate science's biggest needs.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [2] source
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- Interpretationcitation
Solely relying on traditional big data techniques results in dubious findings.
“we highlight research showing that solely relying on traditional big data techniques results in dubious findings”
Quote in the paperNot a substantial claim - Premisecitation
The climate science community faces the significant challenge of dealing with a continuously changing observing system.
“Despite having an abundance of data, the climate science community faces the significant challenge of dealing with a continuously changing observing system.12”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [12] source
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- Premisecitation
Before satellite monitoring became routine in the late 1970s, tropical cyclones were prone to be missed if they were not observed through landfall, a ship, or airplane reconnaissance.
“However, before satellite monitoring became routine in the late 1970s, tropical cyclones were prone to be missed if they were not observed through landfall, a ship, or airplane reconnaissance.13,14”
Quote in the paperSubstantial claim0 of 2 sources checked - Interpretationreasoning
Even high correlations between time series of length 10 could occur by random chance and must be scrutinized further.
“Hence, even these high correlations could occur by random chance and must be scrutinized further.”
Quote in the paperSubstantial claim - Interpretationreasoning
A hurricane cannot be represented with a binary value.
“Thus, one cannot represent a hurricane with a binary value as it does not simply appear and then disappear.”
Quote in the paperSubstantial claim - Interpretationreasoning
Numerous climate phenomena cannot be represented in attribute-value form.
“However, numerous climate phenomena cannot be represented in attributevalue form.”
Quote in the paperSubstantial claim - Premisecitation
Traditional data science and machine learning have relied on attribute-value data as input to most learning models.
“Traditional data science, and machine learning specifically, has relied on attribute-value data as input to most learning models.2”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [2] source
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- Interpretationreasoning
Such a framework generates unstable networks across time due to noisy data as well as the sensitive methods used to construct networks.
“Such a framework generates unstable networks across time due to noisy data as well as the sensitive methods used to construct networks.”
Quote in the paperSubstantial claim - Premisereasoning
Existing data science conventions have focused on a narrow form of data representation that does not abstract spatiotemporal phenomena.
“Second, existing data science conventions have focused on a narrow form of data representation that does not abstract spatiotemporal phenomena.”
Quote in the paperSubstantial claim - Interpretationreasoning
In relationship mining, autocorrelation makes numerous regions tend to be highly correlated due to proximity in space and time.
“In relationship mining, where we are interested in finding significant relationships between datasets, the autocorrelation makes such that numerous regions tend to be highly correlated due to proximity in space and time.”
Quote in the paperSubstantial claim - Interpretationreasoning
There is a greater emphasis on model interpretability rather than model flexibility in climate science.
“First, there is a greater emphasis on model interpretability rather than model flexibility.”
Quote in the paperSubstantial claim- Rests on an assumption: The focus of climate science is more about understanding than predicting.
- Interpretationreasoning
In order for a model to separate between positive (tornadoes) and negative (no tornadoes) events, it must be trained using both populations.
“In order for a model to separate between positive (tornados) and negative (no tornados) events, it must be trained using both populations.”
Quote in the paperSubstantial claim - Interpretationreasoning
Black box models are not adopted in climate science because their accuracy cannot be related back to physical processes.
“Thus, ''black box'' models that tend to do very well in regression or classification tasks are not adopted because their accuracy cannot be related back to physical processes.”
Quote in the paperSubstantial claim - Interpretationreasoning
A shortcoming of Johns et al.'s study is that it failed to sample wind shear and CAPE values when tornadoes did not occur.
“From a data science perspective, a shortcoming of this study is that it failed to sample wind shear and CAPE values when tornados did not occur.”
Quote in the paperSubstantial claim - Premisecitation
Two recent studies published in Nature Climate Change and Nature reached opposing conclusions about whether there are noticeable changes in drought trends due to climate change.
“Two recent studies published in Nature Climate Change 25 and Nature 26 came to opposing conclusions about whether there are noticeable changes in drought trends as a result of climate change.”
Quote in the paperSubstantial claim0 of 2 sources checked - Interpretationreasoning
Existing learning methods that make implicit or explicit independence assumptions about input data will have limited applicability to the climate domain.
“Therefore, existing learning methods that make implicit or explicit independence assumptions about the input data will have limited applicability to the climate domain.”
Quote in the paperSubstantial claim - Interpretationreasoning
Sampling negative events, especially for climate extremes, is no trivial task.
“However, sampling negative events, especially for climate extremes, is no trivial task.”
Quote in the paperSubstantial claim- Rests on an assumption: There is a severe class imbalance between positive and negative events because the majority of the time can be considered a negative event.
- Premisecitation
One study highlighted that the opposing drought results arose partly because each study relied on a different precipitation dataset.
“One study 23 further highlighted the disparate results by pointing out (among other reasons) that each study relied on a different precipitation dataset.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [23] source
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- Interpretationcitation
Google Flu Trends' success did not withstand the test of time.
“Unfortunately, Google Flu Trends' success (and story) did not withstand the test of time.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [30] source
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- Interpretationreasoning
There is a fundamental difference between the objective functions in climate science and broader data science.
“In other words, there is a fundamental difference between the objective functions in climate science and broader data science.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Complex models rarely yield insight into the mechanisms that drive phenomena.
“and they rarely yield insight into the mechanisms that drive phenomena.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Focus on prediction accuracy biases analyses toward more complex statistical models.
“This focus on prediction accuracy biases analyses to more complex statistical models”
Quote in the paperNot a substantial claim - Premisecitation
A 2014 study found that, for consecutive years, Google made more bad predictions than good ones.
“A 2014 study 30 found that, for consecutive years, Google made more bad predictions than good ones.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [30] source
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- Premisecitation
In their study, Faghmous et al. developed an unsupervised method to identify coherent rotating structures in the ocean known as ocean eddies.
“In their study, the authors developed an unsupervised method to identify coherent rotating structures in the ocean known as ocean eddies.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [31] source
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- Premisecitation
The patterns identified in the eddy study were not analyzed based on the simple physical fact that eddies are theoretically axisymmetric Gaussian features with a single extremum.
“However, the patterns identified were not analyzed based on a simple physical fact that eddies are theoretically axisymmetric Gaussian features 33 with a single extremum (maximum or minimum).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [33] source
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- Premisecitation
Faghmous et al. showed that large-scale pattern mining algorithms can discover false patterns if not constrained by basic theoretical facts.
“Faghmous et al. 31 showed that largescale pattern mining algorithms can discover false patterns if not constrained by basic theoretical facts.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [31] source
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- Interpretationreasoning
Some of the most popular data analysis techniques are ill-suited to analyze noisy, heterogeneous, and autocorrelated data found in climate and other sciences.
“some of the most popular data analysis techniques are illsuited to analyze noisy, heterogeneous, and autocorrelated data that are found in climate and other sciences.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Data collected at different times had different purposes, causes, and interpretations.
“As a result, the data that were collected at different times had different purposes, causes, and interpretations.”
Quote in the paperSubstantial claim - Premisecitation
The authors of the Science study explained a physically plausible mechanism by which sea surface temperatures would impact dryness and, as a result, forest fires several months later.
“Instead, the authors were able to explain a physically plausible mechanism that would lead sea surface temperatures to impact dryness, and as a result, forest fires several months later.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [17] source
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- Premisecitation
The Science study linked changes in sea surface temperatures to forest fires in the Amazon.
“As an illustrative example, take the Science study that linked changes in sea surface temperatures to forest fires in the Amazon.17”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [17] source
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- Interpretationreasoning
A hypothesis-driven research agenda will run into problems when there is not sufficient theory to explain certain phenomena.
“On the one hand, a hypothesis-driven research agenda will run into problems when there is not sufficient theory to explain certain phenomena.”
Quote in the paperSubstantial claim - Premisecitation
The link between sea surface temperatures and Amazon fires was not solely based on the 0.9 correlation between a length-10 time series.
“What makes it different from the Google Flu Trends study is that the link between sea surface temperatures and fires was not solely based on the 0.9 correlation between a length-10 time series.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [17] source
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- Interpretationreasoning
Any data-driven discovery is inexorably linked to the quality of the data, their source, and sampling biases.
“First, any data-driven discovery is inexorably linked to the quality of the data, their source, and sampling biases.”
Quote in the paperSubstantial claim - Interpretationreasoning
A theory-driven investigation will likely produce limited insight on how forest fires will evolve under continued warming.
“Thus, a theory-driven investigation will likely produce limited insight on that topic.”
Quote in the paperSubstantial claim - Interpretationreasoning
If it were only for the data-driven insight, the Science study would not be plausible enough.
“If it were only for the data-driven insight, the study would not be plausible enough.”
Quote in the paperNot a substantial claim - Premisecitation
This physical consistency constrained a simple pattern-mining algorithm to produce more accurate features compared to methods that routinely merge eddies and could be identified as features with two or more extrema.
“This physical consistency constrained a simple pattern-mining algorithm to produce more accurate features compared to methods that routinely merge eddies and could be identified as features with two or more extrema.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [31] source
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- Interpretationreasoning
Without a physically consistent hypothesis, testing would yield misleading results.
“showing that without a physically consistent hypothesis, testing would yield misleading results.”
Quote in the paperSubstantial claim- Rests on an assumption: Scientific theory and first principles in climate science are sufficiently reliable and complete to constrain data-driven methods and interpret their results.
- Premisecitation
It has been shown both theoretically and in practice that theory-agnostic models generalize poorly to unseen data despite being highly accurate on observed data.
“Finally, within predictive models, it has been shown both theoretically 35 and in practice 36 that theory-agnostic models generalize poorly to unseen data despite being highly accurate on observed data.”
Quote in the paperSubstantial claim0 of 2 sources checked- Rests on an assumption: Scientific theory and first principles in climate science are sufficiently reliable and complete to constrain data-driven methods and interpret their results.
- Premisecitation
Methods that fit the data without any regard to the underlying physical dynamics that constrain and produce the observations can lead to a 'nonphysical finite time blow-up and large time instability in statistical solutions.'
“As a result, methods that fit the data without any regard to the underlying physical dynamics that constrain and produce the observations can lead to a ''nonphysical finite time blow-up and large time instability in statistical solutions.''35”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [35] source
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- Premisereasoning
Any high statistics observed in the relationship mining study were found to be explainable by chance.
“Thus, any high statistics observed were found to be explainable by chance”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [34] source
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- Premisecitation
The authors showed that although many candidate vectors were identified as skilled predictors using traditional significance testing methods, all of the potential 41,741 relationships were not statistically significant once they accounted for dependence between models, variables, locations, and seasons.
“The authors showed that although many of the candidate vectors were identified as skilled predictors using traditional significance testing methods, all of the potential 41,741 relationships were not statistically significant once they accounted for dependence between models, variables, locations, and seasons.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [34] source
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- Premisecitation
In relationship mining, scientists attempted to automatically identify climate variables that are good predictors of simulated increases in global temperatures if CO2 levels in the atmosphere were to double (equilibrium climate sensitivity).
“In a relationship mining application, 34 scientists attempted to automatically identify the various climate variables that are currently observable and are good predictors of simulated increases in global temperatures if CO 2 levels in the atmosphere were to double (known as equilibrium climate sensitivity).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [34] source
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- Classified as probably not a substantial claim. 42
- May combine two claims. 15
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InterpretationreasoningThe discrepancy between data science's success in other fields and its limited impact in climate science stems from the complex nature of climate data and the scientific questions climate science brings forth.
“This discrepancy stems from the complex nature of climate data as well as the scientific questions climate science brings forth.”
Quote in the paperNot a substantial claim- Rests on an assumption: Earth is a complex dynamical system like none we have studied before.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Premisenone givenClimate science, its data, and challenges have not been exposed to the broader data science community until recently.
“Finally, and this is only a matter of time, climate science, its data, and challenges have not been exposed to the broader data science community until recently.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
InterpretationreasoningBig data-induced progress within climate science has been slower compared with big data's success in other fields such as biology or advertising.
“As a result, big data-induced progress within climate science has been slower compared with big data's success in other fields such as biology or advertising.”
Quote in the paperNot a substantial claim- Rests on an assumption: Earth is a complex dynamical system like none we have studied before.
May combine two claims.
PremisecitationThere is an upward trend in the total number of tropical cyclones in the Atlantic, but it is unclear if it is due to changes in the observational system or due to climate change.
“Thus, there is an upward trend in the total number of tropical cyclones in the Atlantic, but it is unclear if it is due to changes in the observational system or due to climate change.15”
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PremisecitationClimate data sources span four diverse sources: in situ, remote sensed, model output, and paleoclimatic.
“Climate data sources span across four diverse sources: in situ, remote sensed, model output, and paleoclimatic.11”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [11]
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InterpretationreasoningData science has had little impact on furthering understanding of the planet despite the abundance of climate data.
“Despite the urgency, data science has had little impact on furthering our understanding of our planet in spite of the abundance of climate data.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
PremisereasoningOne data-centric challenge is how to merge data from heterogeneous sources that are complimentary yet possibly redundant.
“One data-centric challenge is how to merge data from heterogeneous sources that are complimentary yet possibly redundant.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationThe duration of the fire record only spanned 10 years, and these correlations were between time series of length 10.
“However, the duration of the fire record only spanned 10 years, and these correlations were between time series of length 10.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [17] source
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Premisenone givenVariables that rely on ground stations might be subject to spatial bias or have to be moved frequently because of sociopolitico events on the ground.
“For example, variables that rely on ground stations might be subject to spatial bias or have to be moved frequently because of sociopolitico events on the ground.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
InterpretationreasoningSpatiotemporal evolutionary processes are well captured by differential equations used in climate models, but there is a need for similar abstractions within broader data science.
“Such spatiotemporal evolutionary processes are well captured by the differential equations used in climate models, but there is a need for similar abstractions within broader data science.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based).
Premisenone givenThe most common methods currently construct climate networks for each time slice and then analyze them separately.
“The most common methods currently construct networks for each time slice and then analyze them separately.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationThe study of networks in a variety of applications assumes static networks, with some notable exceptions.
“Finally, the study of networks in a variety of applications assumes static networks, with some notable exceptions.21”
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PremisecitationA study in Science found that sea surface temperatures thousands of miles away had a 0.9 correlation with forest fires in certain regions of the Amazon.
“The study found that the sea surface temperatures thousands of miles away had a 0.9 correlation with forest fires in certain regions of the Amazon.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [17] source
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PremisecitationMost climate networks assume single-variable networks, while the climate system is multivariate.
“linear correlation, 6,18 mutual information, 19 synchronization, 20 assume single-variable networks, while the climate system is a multivariate one.”
Quote in the paperSubstantial claim0 of 4 sources checkedProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone givenHeavy postprocessing of climate data leads to biases in the data, and any analysis must appropriately identify how such biases might manifest in the results.
“Such heavy postprocessing will lead to biases in the data, and any analysis must appropriately identify how such biases might manifest in the results.”
Quote in the paperNot a substantial claim- Rests on an assumption: Climate data are routinely postprocessed for easy use.
Probably not a substantial claim: may not pass S4 (based).
Premisenone givenEach variable in a climate data analysis might have different origins and biases.
“Each variable in the analysis might have different origins and biases.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
Interpretationnone givenAlthough climate data originate from disparate sources, they are still part of the same system and are inexorably related.
“From a data science perspective, it is important to be aware that although these data originate from disparate sources, they are still part of the same system and are inexorably related.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Premisenone givenOther ECVs might come from satellites whose missions generally last 5 years, where understanding how the records of the same dataset relate to each other is a significant challenge to a continuous record.
“Other ECVs might come from satellites whose missions generally last 5 years, where understanding how the records of the same dataset relate to each other is a significant challenge to a continuous record.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenA hurricane is an evolving pattern over a significant spatiotemporal span.
“For example, a hurricane is an evolving pattern over a significant spatiotemporal span.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationJohns et al. analyzed wind shear and CAPE associated with 242 tornado cases and fit a model to these data.
“The authors analyzed the wind shear and CAPE associated with 242 tornado cases and fit a model to these data.”
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PremisecitationThere are numerous types of droughts: agricultural, meteorological, and hydrological.
“First, there are numerous types of droughts: agricultural, metrological, and hydrological.23”
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Interpretationnone givenEven if a definition of drought is agreed upon, how to represent precipitation deficiency is unclear.
“Second, even if we can agree on a definition, how to represent such ''deficiency'' is unclear.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningIn autocorrelated spatiotemporal fields, data tend to be smooth and feature boundaries become blurred, making pattern mining challenging.
“For example, pattern mining-the task of autonomously extracting interesting patterns from datais challenging because, in autocorrelated spatiotemporal fields, the data tend to be smooth and feature boundaries become blurred.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningDroughts may be quantified in absolute or relative terms, and the chosen data representation can lead to different conclusions.
“droughts may be quantified in absolute or relative terms, and depending on which data representation is chosen, one might arrive at different conclusions.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based).
Premisenone givenThe spatiotemporal nature of climate data emerges in the form of auto- and cross-correlation between input variables.
“The spatiotemporal nature of climate data emerges in the form of auto-and cross-correlation between input variables.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationJohns et al. investigated the genesis conditions of tornadoes by monitoring wind shear and convective available potential energy (CAPE).
“For example, Johns et al. 22 investigated the genesis conditions of tornadoes by monitoring two environmental conditions: wind shear and convective available potential energy (CAPE).”
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Interpretationnone givenThe majority of network analyses in climate rely on network characteristics derived from mathematics and other domains, which might not be suitable for the application at hand.
“The majority of network analyses in climate rely on network characteristics derived from mathematics and other domains, which might not be suitable for the application at hand.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.
InterpretationreasoningUnsupervised big data analysis is a tool, and like any tool, it is prone to misuse.
“What we have learned since the Wired article is that unsupervised big data analysis is a tool, and like any tool, it is prone to misuse.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationIn 2009, a team of Google researchers reported in Nature that by analyzing flu-related queries, it can accurately detect flu outbreaks quicker than the CDC.
“In 2009, a team of Google researchers reported in the journal Nature 29 that by analyzing flu-related queries coming into its search engine, it can accurately detect flu outbreaks quicker than the Centers for Disease Control and Prevention.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [29] source
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Interpretationnone givenThe big data narrative gained common acceptance in mainstream culture and data sciences.
“This big data narrative gained common acceptance in mainstream culture and data sciences.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationA 2008 Wired Magazine article declared big data as 'The End of Theory,' where data would speak for themselves when present in sufficiently large quantities.
“Such a narrative became so popular that a 2008 article in Wired Magazine went as far as declaring big data as ''The End of Theory,'' 28 where data would speak for themselves when present in sufficiently large quantities.”
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InterpretationreasoningIt is less clear how much an increase or decrease in root-mean-square error would impact climate phenomena, much less societies on the ground.
“However, it is less clear how much an increase or decrease in root-mean-square error would impact climate phenomena, much less societies on the ground.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenAs models become more complex, it becomes harder to understand why they are skillful.
“but as models become more complex, it becomes harder to understand why they are skillful”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningThe focus on abstract objective metrics makes it hard to apply data-driven methods to climate, where the emphasis is more on understanding than mere statistical accuracy.
“However, the focus on abstract quantities such as these makes it hard to apply these methods to climate where the emphasis is more about understanding than mere statistical accuracy.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningBoth groups used the same data but relied on different data analysis techniques to arrive at opposing conclusions.
“In this case, both groups used the same data but instead relied on different data analysis techniques to arrive at opposing conclusions.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based).
Premisenone givenData-driven methods generally focus on optimizing objective metrics to quantify algorithm performance, such as revenue, churn rate, or classification error.
“Data-driven methods generally focus on optimizing objective metrics to quantify the performance of an algorithm: revenue, churn rate, classification error, etc.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationAccording to the 2014 study's authors, the drop in Google Flu Trends accuracy was due to the system's overreliance on search queries, which depend on Google's ever-changing search engine algorithm.
“According to the study's authors, the drop in accuracy was due to the system's overreliance on search queries, which depend on Google's ever-changing search engine algorithm.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [30] source
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Interpretationnone givenAdvances in data science, growth of datasets, and virtually no computational barriers gave rise to the notion that any problem with a clear objective function can be solved given sufficient data.
“Advances in data science, the growth of datasets, and virtually no (computational) barriers to the analysis of such data gave rise to the notion that any problem with a clear objective function can be solved given sufficient data.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationIn examining changes in sea surface temperatures and hurricane occurrences, one study found observed hurricane trends were part of larger oscillations.
“When examining the changes in sea surface temperatures and hurricane occurrences, one study 27 found that the observed hurricane trends were just part of larger oscillations”
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PremisecitationAnother study concluded that hurricane counts were increasing along with sea surface temperatures.
“whereas another study 15 concluded that hurricane counts were increasing along with sea surface temperatures.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [15] source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationThere were several existing methods that identified eddies, including one set of results published in Science.
“There were several existing methods that identified eddies, including one set of results that were published in the journal Science.32”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [32] source
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone givenWe do not have a clear theory about how forest fires will evolve if the planet continues to warm.
“For example, we do not have a clear theory about how forest fires will evolve if the planet continues to warm.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningWhile these methods will certainly produce output, they may rarely yield insight.
“Thus, while these methods will certainly produce output, they may rarely yield insight.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningThe riskier offense is interpreting such output using a theory-free mindset.
“The riskier offense is that of interpreting such output using a theory-free mindset.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationreasoningFocusing solely on statistical accuracy without any regard to climate science theory may return physically implausible results that have no practical use to the domain scientist.
“On the other hand, focusing solely on statistical accuracy without any regard to climate science theory may return physically implausible results that have no practical use to the domain scientist.”
Quote in the paperNot a substantial claim- Rests on an assumption: Scientific theory and first principles in climate science are sufficiently reliable and complete to constrain data-driven methods and interpret their results.
Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.
InterpretationreasoningThe two most common perils of big data analytics in scientific domains deal with the methods used and the means by which results are interpreted, and they share a common thread: disregard for common domain knowledge.
“More broadly, the two most common perils of big data analytics when it comes to scientific domains deal with the methods used and the means by which results are interpreted-and they share a common thread: disregard for common domain knowledge.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone givenBig data analytics alone are not enough to insightfully and accurately explore climate data.
“However, big data analytics alone are not enough to insightfully and accurately explore climate data.”
Quote in the paperNot a substantial claim- Rests on an assumption: Scientific theory and first principles in climate science are sufficiently reliable and complete to constrain data-driven methods and interpret their results.
- Rests on an assumption: Domain knowledge can be encapsulated in data science methods to produce theoretically-consistent results.
Classified as probably not a substantial claim.
PremisecitationThe researchers set up a search space comprised of 41,741 vectors representing data from various spatiotemporal scales.
“To identify such variables, the researchers set up a search space comprised of 41,741 vectors representing data from various spatiotemporal scales.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [34] source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationGanguly et al. provide another example of theory-guided data science in the context of climate extremes.
“Ganguly et al. 37 provide another example of this paradigm in the context of climate extremes.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [37]
Not checked in the free run.
- Rests on an assumption: The cited examples of theory-guided data science are representative enough to support the general conclusion that theory-guided data science is needed for climate science.
The paper relies on this without support.
Earth is a complex dynamical system like none we have studied before.
“However, Earth is a complex dynamical system like none we have studied before.”
The paper relies on this without support.
A lot remains to be understood about our planet and the physical processes that govern it to effectively answer questions about global climate change and its societal impacts.
“A lot remains to be understood about our planet and the physical processes that govern it to effectively answer questions about global climate change and its societal impacts.”
The paper relies on this without support.
The majority of climate data are organized in a spatiotemporal grid.
“For example, the majority of climate data are organized in a spatiotemporal grid.”
The paper relies on this without support.
Climate data are auto-correlated where regions in spatial or temporal proximity tend to be highly related.
“As such, the data are auto-correlated where regions in spatial or temporal proximity tend to be highly related.”
The paper relies on this without support.
Some climate datasets span only a decade or less.
“Some datasets span only a decade or less.”
The paper relies on this without support.
The Earth system is composed of numerous interacting variables that guide its climate.
“The Earth system is composed of numerous interacting variables that guide its climate.”
The paper relies on this without support.
Different key climate variables are monitored using various technologies, and some might not be observed at all.
“Different key variables are monitored using various technologies, and some might not be observed at all.”
The paper relies on this without support.
The 50 essential climate variables (ECVs) each have their own biases.
“One attempt to quantify the most important Earth science variables is the 50 essential climate variables (ECVs), each of which has its own biases.”
The paper relies on this without support.
Climate data are routinely postprocessed for easy use.
“Climate data are routinely postprocessed for easy use.”
The paper relies on this without support.
The focus of climate science is more about understanding than predicting.
“However, the focus of climate science is more about understanding than predicting.”
The paper relies on this without support.
There is a severe class imbalance between positive and negative events because the majority of the time can be considered a negative event.
“This is because there is a severe class imbalance between positive and negative events since the majority of the time can be considered a negative event.”
The paper relies on this without support.
Scientific theory and first principles in climate science are sufficiently reliable and complete to constrain data-driven methods and interpret their results.
“What is needed is an approach that leverages the advances in data-driven research yet constrains both the methods and the interpretation of the results through tried-and-true scientific theory.”
The paper relies on this without support.
Domain knowledge can be encapsulated in data science methods to produce theoretically-consistent results.
“Thus, to make significant contributions to climate science, new data science methods must encapsulate domain knowledge to produce theoretically-consistent results.”
The paper relies on this without support.
The cited examples of theory-guided data science are representative enough to support the general conclusion that theory-guided data science is needed for climate science.
“Here we highlight some other examples of theory-guided data science with climate applications.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Figure 1 attribution points to the wrong reference
MinorThe figure caption attributes its source to the IPCC using reference 38, but reference 38 is a paper by Ganguly et al. The IPCC entry is reference 39.
“Figure from Intergovernmental Panel on Climate Change (IPCC).38”
“38. Ganguly AR, et al. Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques. Nonlinear Proc Geophys Discus 2014; 1:51-96.”
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Sorted by citations
Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data
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“These efforts have been complemented with recent review papers [8], [31], [32], [33], workshops (e.”
BackgroundResultSelf-citationTechnology Roadmap for Flexible Sensors
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Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
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“Early results in isolated and relatively simple scenarios are promising, and the expectations are rising for this paradigm to accelerate scientific discovery and help address some of the biggest challenges that are facing humanity in terms of climate [87], health [280], and food security [129].”
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Self-citationThe Future of Earth Observation in Hydrology.
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“As previously investigated in diverse disciplines (Faghmous and Kumar, 2014; Fischer et al., 2006; Wagner and Rondinelli, 2016), theory-guided ML can be developed through a variety of approaches (Karpatne et al., 2017).”
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“…forces come from data-intensive and data-rich areas such as astronomy [Borne et al. 2010], neurobiology [Dierick and Gabbiani 2015], climate change [Faghmous and Kumar 2014], research assessment [Siart et al. 2015], media and entertainment [Gold et al. 2013], supply chain management (SCM) [Hazena…”
BackgroundBig data meet green challenges: big data toward green applications
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“Faghmous and Kumar [10] discussed some challenges and opportunities on mining large climate data, and propose a data science paradigm theory guided by other scientific theories to constrain both the big data techniques as well as the relevant interpretation process to extract accurate insight from large climate data.”
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“, 2013) with complicated causal and non-causal connections (Faghmous and Kumar, 2014).”
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“…/erae/article/47/3/849/5552525 by guest on 02 D ecem ber 2021 young research field, with several contributions from climate and material science (Faghmous and Kumar, 2014; Faghmous et al., 2014; Ganguly et al., 2014; Wagner and Rondinelli, 2016; Karpatne et al., 2017; Sheikh and Jahirabadkar,…”
BackgroundData Science: A Comprehensive OverviewPreprint
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“…forces come from data-intensive and data-rich areas such as astronomy [Borne et al. 2010], neurobiology [Dierick and Gabbiani 2015], climate change [Faghmous and Kumar 2014], research assessment [Siart et al. 2015], media and entertainment [Gold et al. 2013], supply chain management (SCM) [Hazena…”
BackgroundVegetation anomalies caused by antecedent precipitation in most of the world
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Tackling climate change with machine learning
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Smart city big data analytics: An advanced review
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Computational sustainability: computing for a better world and a sustainable future
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Challenges and Opportunities in Calibrating Low-Cost Environmental Sensors
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“Climate change is a significant challenge for environmental sustainability [1,2], and to address this issue effectively, it is crucial to advance scientific knowledge by collecting and comprehending information on various aspects related to climate change [3].”
BackgroundBig data has big potential for applications to climate change adaptation
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“Understanding the big data (or data empathy) according to [3]) is a challenging task considering its 5Vs feature (volume, variety, velocity, veracity and value), revealing the hidden valuable information from big data requires adequate knowledge of the purposes of corresponding data as well as the…”
BackgroundInfluentialAssessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change
Wi, S., & Steinschneider, S. (2022) · Water Resources Research · doi:10.1029/2022wr032123 · Cited by 137 · see citing works
“Physics-informed machine learning (PIML; Faghmous & Kumar, 2014; Karniadakis et al., 2021; Karpatne et al., 2017; Jiang et al., 2020; Reichstein et al., 2019; Willard et al., 2022) presents another avenue that could further bolster the use of DL hydrologic models for climate change projections.”
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“Less precipitation, the rise in temperature, increase in the evaporation rate, and reduction in soil moisture are key indicators of agriculture drought triggers [9,10].”
BackgroundA review of drought monitoring with big data: Issues, methods, challenges and research directions
Balti, H., Abbes, A. B., Mellouli, N., Farah, I. R., Sang, Y., & Lamolle, M. (2020) · Ecological Informatics · doi:10.1016/j.ecoinf.2020.101136 · Cited by 112 · see citing works
A machine learning model that outperforms conventional global subseasonal forecast models
Chen, L., Zhong, X., Li, H., Wu, J., Lu, B., Chen, D., Xie, S.-P., Wu, L., Chao, Q., Lin, C., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50714-1 · Cited by 105 · see citing works
Rogge, N., Agasisti, T., & De Witte, K. (2017) · Public Policy and Administration · doi:10.1177/0952076716687355 · Cited by 98 · see citing works
“…Torenvlied, 2016), combatting crime and fraud (Chen and Zhang, 2014), health and well-being (Ginsberg et al., 2009), environment and sustainability (Faghmous and Kumar, 2014), transportation (Kim et al., 2014), energy (Diamantoulakis et al., 2015), smart cities (Hashem et al., 2016; Morabito,…”
BackgroundHoyos, I. P., Krakauer, N. Y., Khanbilvardi, R. M., & Armstrong, R. A. (2016) · Geosciences · doi:10.3390/geosciences6020017 · Cited by 92 · see citing works
Sebestyén, V., Czvetkó, T., & Abonyi, J. (2021) · Frontiers in Environmental Science · doi:10.3389/fenvs.2021.619092 · Cited by 97 · see citing works
Mobile phone network data reveal nationwide economic value of coastal tourism under climate change
Kubo, T., Uryu, S., Yamano, H., Tsuge, T., Yamakita, T., & Shirayama, Y. (2019) · Tourism Management · doi:10.1016/j.tourman.2019.104010 · Cited by 88 · see citing works
Chauhan, R., Kaur, H., & Alankar, B. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103239 · Cited by 87 · see citing works
Big Earth data analytics: a survey
Yang, C. P., Yu, M., Li, Y., Hu, F., Jiang, Y., Liu, Q., Sha, D., Xu, M., & Gu, J. (2019) · Big Earth Data · doi:10.1080/20964471.2019.1611175 · Cited by 85 · see citing works
“…data obtained can be used to understand our Earth system as a whole for addressing scienti fi c challenges: such as climate change and global warming (Faghmous & Kumar, 2014), increasing intensity and frequent of disasters and better preparedness for dealing with them (Yu, Huang, Qin, Scheele, &…”
BackgroundMethodInfluentialA Gaussian process based big data processing framework in cluster computing environment
Manogaran, G., & Lopez, D. (2017) · Cluster Computing · doi:10.1007/s10586-017-0982-5 · Cited by 83 · see citing works
“Recently, various big data analytical approaches have been used to monitor the climate change and predict various diseases [13–15].”
BackgroundIsraelsen, B. W., & Ahmed, N. R. (2019) · ACM Computing Surveys · doi:10.1145/3267338 · Cited by 81 · see citing works
“for scientic data analysis [37], public policy and medicine [64, 138] and cognitive assistance [52].”
BackgroundMethodA Review of Physics-Based, Data-Driven, and Hybrid Models for Tool Wear Monitoring
Zhang, H., Jiang, S., Gao, D., Sun, Y., & Bai, W. (2024) · Machines · doi:10.3390/machines12120833 · Cited by 83 · see citing works
Applying big data beyond small problems in climate research
Knüsel, B., Zumwald, M., Baumberger, C., Hadorn, G. H., Fischer, E. M., Bresch, D. N., & Knutti, R. (2019) · Nature Climate Change · doi:10.1038/s41558-019-0404-1 · Cited by 82 · see citing works
Monitoring sustainable development by means of earth observation data and machine learning: a review
Ferreira, B., Iten, M. D. C., & Silva, R. (2020) · Environmental Sciences Europe · doi:10.1186/s12302-020-00397-4 · Cited by 82 · see citing works
Spatial and temporal epidemiological analysis in the Big Data era
Pfeiffer, D. U., & Stevens, K. (2015) · Preventive Veterinary Medicine · doi:10.1016/j.prevetmed.2015.05.012 · Cited by 77 · see citing works
“This extreme view has resulted in some debate (Norvig, 2009; Pigliucci, 2009; Schutt and O’Neil, 2013; Faghmous and Kumar, 2014; Mayer-Schönberger and Cukier, 2014).”
Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes
Arora, S., Yang, B., Eyuboglu, S., Narayan, A., Hojel, A., Trummer, I., & Ré, C. (2023) · Proceedings of the VLDB Endowment · doi:10.14778/3626292.3626294 · Cited by 93 · see citing works
“Organizations often seek insights trapped in heterogeneous data lakes ( e.g. the web, corporate data lakes, and electronic health records) [10, 26, 54].”
BackgroundDisease Surveillance System for Big Climate Data Processing and Dengue Transmission
Manogaran, G., & Lopez, D. (2017) · International Journal of Ambient Computing and Intelligence · doi:10.4018/ijaci.2017040106 · Cited by 72 · see citing works
“Recently, Faghmous & Kumar (2014) have identified challenges and opportunities to mine large climate datasets using big data technologies.”
BackgroundDistinguishing Trends and Shifts from Memory in Climate Data
Beaulieu, C., & Killick, R. (2018) · Journal of Climate · doi:10.1175/jcli-d-17-0863.1 · Cited by 68 · see citing works
“Climate science has typically put greater emphasis on statistical model interpretability rather than flexibility because focus is more on a system-level understanding rather than prediction of single events (Faghmous and Kumar 2014).”
BackgroundRodríguez‐González, A., Zanin, M., & Menasalvas, E. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677910 · Cited by 67 · see citing works
Integrating scientific knowledge into machine learning using interactive decision trees
Sarailidis, G., Wagener, T., & Pianosi, F. (2022) · Computers & Geosciences · doi:10.1016/j.cageo.2022.105248 · Cited by 64 · see citing works
In-Mapper combiner based MapReduce algorithm for processing of big climate data
Manogaran, G., Lopez, D., & Chilamkurti, N. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.02.048 · Cited by 54 · see citing works
AI and Data Democratisation for Intelligent Energy Management
Marinakis, V., Koutsellis, T., Νίκας, Α., & Doukas, H. (2021) · Energies · doi:10.3390/en14144341 · Cited by 53 · see citing works
“Several applications of data democratisation can also be found in the field of energy resource, including data democratisation in offshore drilling [56], oil and gas operations [57,58], as well as earth [59] and climate [60] sciences.”
Johnson, R. S. (2017) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2017.0092 · Cited by 47 · see citing works
Big data for climate action or climate action for big data?
Espinoza, M. I., & Aronczyk, M. (2021) · Big Data & Society · doi:10.1177/2053951720982032 · Cited by 46 · see citing works
“As Faghmous and Kumar (2014) note, current climate data sources present significant challenges for researchers.”
BackgroundBridging sustainability science, earth science, and data science through interdisciplinary education
Pennington, D. D., Ebert‐Uphoff, I., Freed, N., Martin, J. E., & Pierce, S. A. (2019) · Sustainability Science · doi:10.1007/s11625-019-00735-3 · Cited by 45 · see citing works
“In fact, the growing field of theory-guided data science is based on the idea that new DS approaches for ESS applications are best developed in collaboration with scientists and that the most effective DS approaches are achieved by incorporating domain knowledge (such as physical constraints or relationships) in their…”
BackgroundTheory-Guided Data Science for Climate Change
Faghmous, J. H., Banerjee, A., Shekhar, S., Steinbach, M., Kumar, V., Ganguly, A. R., & Samatova, N. (2014) · Computer · doi:10.1109/mc.2014.335 · Cited by 42 · see citing works
Self-citationAugmented Convolutional LSTMs for Generation of High-Resolution Climate Change Projections
Harilal, N., Singh, M. P., & Bhatia, U. (2021) · IEEE Access · doi:10.1109/access.2021.3057500 · Cited by 41 · see citing works
“To characterize these uncertainties, ESMs are often executed with multiple initial conditions, multiple ensemble mode with different RCPs making impact relevant insight discovery from ESM outputs a “big data” challenge [15].”
BackgroundClimate Changes through Data Science: Understanding and Mitigating Environmental Crisis
Ali, A. H., & Thakkar, R. (2023) · Mesopotamian Journal of Big Data · doi:10.58496/mjbd/2023/017 · Cited by 40 · see citing works
“The emerging field of climate informatics now marshals the full scope computational data science to drive discovery and solutions in the climate realm[5].”
BackgroundGlobal hydro-climatic biomes identified via multitask learning
Papagiannopoulou, C., Miralles, D. G., Demuzere, M., Verhoest, N. E. C., & Waegeman, W. (2018) · Geoscientific model development · doi:10.5194/gmd-11-4139-2018 · Cited by 38 · see citing works
“In recent years, the exponential advance in Earth observation research has made climate science one of the most data-rich scientific domains (Faghmous and Kumar, 2014).”
BackgroundBriefing: The 2021 Glasgow Climate Pact: steps on the transition pathway towards a low carbon world
Cohen, R., Eames, P., Hammond, G. P., Newborough, M., & Norton, B. (2022) · Proceedings of the Institution of Civil Engineers - Energy · doi:10.1680/jener.22.00011 · Cited by 38 · see citing works
Flow Pattern Transition in Pipes Using Data-Driven and Physics-Informed Machine Learning
Quintino, A. M., da Rocha, D. L. L. N., da Fonseca, R., & Rodríguez, O. M. H. (2020) · Journal of Fluids Engineering · doi:10.1115/1.4048876 · Cited by 37 · see citing works
Chen, K., Torbaghan, M. E., Thom, N. H., & Faramarzi, A. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109922 · Cited by 36 · see citing works
Oxoli, D., Ronchetti, G., Minghini, M., Molinari, M. E., Lotfian, M., Sona, G., & Brovelli, M. A. (2018) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi7110421 · Cited by 36 · see citing works
“Climate change adaptation and mitigation strategies must certainly start from collecting, understanding, and extracting knowledge from the multitude of geo-data sources available for cities [9].”
BackgroundBrönnimann, S., & Wintzer, J. (2018) · Wiley Interdisciplinary Reviews Climate Change · doi:10.1002/wcc.559 · Cited by 36 · see citing works
“According to Faghmous and Kumar (2014) “every dataset has a story, and understanding it can guide the choice of suitable analyses; some have labeled this data understanding as data empathy.””
BackgroundEdge Detection Reveals Abrupt and Extreme Climate Events
Bathiany, S., Hidding, J., & Scheffer, M. (2020) · Journal of Climate · doi:10.1175/jcli-d-19-0449.1 · Cited by 36 · see citing works
Ladi, T., Jabalameli, S., & Sharifi, A. (2022) · Environment and Planning B Urban Analytics and City Science · doi:10.1177/23998083221085281 · Cited by 38 · see citing works
“Climate data are increasingly abundant due to technological advancements, model simulations, sensors, and observers (Faghmous and Kumar, 2014; Koc and Acar 2021).”
BackgroundPhysics-guided machine learning from simulated data with different physical parameters
Chen, S., Kalanat, N., Xie, Y., Li, S., Zwart, J. A., Sadler, J. M., Appling, A., Oliver, S. K., Read, J. S., & Jia, X. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-01864-z · Cited by 32 · see citing works
Data Science: Nature and Pitfalls
Cao, L. (2016) · IEEE Intelligent Systems · doi:10.1109/mis.2016.86 · Cited by 31 · see citing works
“…by connecting information from the underlying markets, social media, other financial markets, socio-economic data and policies [7]; • Predicting local climate change and effect by connecting local, regional and global climate data, geographical data, agricultural data and other information [28].”
BackgroundMachine learning for healthcare that matters: Reorienting from technical novelty to equitable impact
Balagopalan, A., Baldini, I., Celi, L. A., Gichoya, J. W., McCoy, L. G., Naumann, T., Shalit, U., van der Schaar, M., & Wagstaff, K. L. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000474 · Cited by 31 · see citing works
“Researchers must seek to achieve “data empathy” [40], and understand the intensely human processes and stories that data—particularly data in healthcare—embed [41].”
BackgroundZhang, X., Hou, L., Liu, J., Yang, K., Chai, C., Li, Y., & He, S. (2022) · Energy · doi:10.1016/j.energy.2022.124382 · Cited by 29 · see citing works
Investigation of urban climates and built environment relations by using machine learning
Koc, M., & Acar, A. (2021) · Urban Climate · doi:10.1016/j.uclim.2021.100820 · Cited by 28 · see citing works
Greis, N. P., Nogueira, M. L., Bhattacharya, S., Spooner, C., & Schmitz, T. L. (2022) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-022-01999-w · Cited by 28 · see citing works
“These include physical phenomena such as turbulent flow (Singh et al., 2017) and geoscience applications such as hydrologic modeling and climate change (Karpatne et al., 2017; Sheikh & Jahirabadkar, 2018; Faghmous & Kumar, 2014).”
Reliability of ERA5 and ERA5-Land reanalysis data in the Canadian Prairies
Gheysari, A. F., Maghoul, P., Ojo, E. R., & Shalaby, A. (2023) · Theoretical and Applied Climatology · doi:10.1007/s00704-023-04771-z · Cited by 28 · see citing works
“On the other hand, satellite data are subjected to temporal discontinuity due to cloud obstruction, changes in orbit, and short lifespan (Faghmous and Kumar 2014).”
Integrating Domain Knowledge in Data-Driven Earth Observation With Process Convolutions
Svendsen, D. H., Piles, M., Munoz-Mari, J., Luengo, D., Martino, L., & Camps‐Valls, G. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3059550 · Cited by 26 · see citing works
“In recent years, applications have been introduced in various subfields of the Earth sciences: in climate science [8], [9],”
BackgroundSchäfer, M. S., & Hase, V. (2022) · Wiley Interdisciplinary Reviews Climate Change · doi:10.1002/wcc.806 · Cited by 27 · see citing works
“Similar to discussions in climate science (Faghmous & Kumar, 2014; Knüsel et al., 2019), we argue for an integrative, reflexive approach to propel computational research on climate change communication forward.”
BackgroundMethodLuo, M., Meng, F., Wang, Y., Sa, C., Duan, Y., Bao, Y., & Liu, T. (2023) · Journal of Hydrology · doi:10.1016/j.jhydrol.2023.129673 · Cited by 25 · see citing works
Rezaiy, R., & Shabri, A. (2025) · Journal of Hydroinformatics · doi:10.2166/hydro.2025.292 · Cited by 22 · see citing works
Balti, H., Abbes, A. B., Sang, Y., Mellouli, N., & Farah, I. R. (2023) · Computers & Geosciences · doi:10.1016/j.cageo.2023.105435 · Cited by 21 · see citing works
Wang, Y., Zhang, L., Erichson, N. B., & Yang, T. (2025) · Journal of Hydrology · doi:10.1016/j.jhydrol.2025.133161 · Cited by 21 · see citing works
A Hierarchical Pattern Learning Framework for Forecasting Extreme Weather Events
Wang, D., & Ding, W. (2015) · Proceedings · doi:10.1109/icdm.2015.93 · Cited by 21 · see citing works
“However, comparing with other domains like electronic advertising, the success of big-data-induced process in studying climate system is limited [2].”
BackgroundCuration and Analysis of Global Sedimentary Geochemical Data to Inform Earth History
Mehra, A. K., Keller, C. B., Zhang, T., Tosca, N. J., McLennan, S. M., Sperling, E. A., Farrell, Ú. C., Brocks, J. J., Canfield, D. E., Cole, D. B., et al. (2021) · GSA Today · doi:10.1130/gsatg484a.1 · Cited by 21 · see citing works
“…Earth sciences, and, with respect to volume, many Earth history and geochemistry compilations are small in comparison to the datasets used in other subdisciplines, including seismology (e.g., Nolet, 2012), climate science (e.g., Faghmous and Kumar, 2014), and hydrology (e.g., Chen and Wang, 2018).”
BackgroundAlhajeri, M. S., Alnajdi, A., Abdullah, F., & Christofides, P. D. (2022) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2022.12.001 · Cited by 21 · see citing works
“…been encouraging, and expectations are growing that this paradigm will speed up scientific advancement and aid in resolving some of the global challenges with regards to the environment (Faghmous and Kumar, 2014), healthcare (Wang et al., 2020a), and food and nutrition security (Jia et al., 2019).”
BackgroundEarth Observation Satellite Imagery Information Based Decision Support Using Machine Learning
Ferreira, B., Silva, R., & Iten, M. D. C. (2022) · Remote Sensing · doi:10.3390/rs14153776 · Cited by 22 · see citing works
Corporate accountability and big data analytics: is non-financial disclosure a missing link?
Agostini, M., Arkhipova, D., & Mio, C. (2023) · Sustainability Accounting Management and Policy Journal · doi:10.1108/sampj-02-2022-0110 · Cited by 20 · see citing works
Karpatne, A., & Kumar, V. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3105810 · Cited by 20 · see citing works
“This theory-guided data science paradigm [5, 11] is expected to be a key enabler in advancing our knowledge of the Earths climate system and informing adaptation and”
MethodSelf-citationPredicting Carbon Dioxide Emissions with the Orange Application: An Empirical Analysis
Salem, I. E., & Abdulqader, A. W. (2023) · Mesopotamian Journal of Computer Science · doi:10.58496/mjcsc/2023/009 · Cited by 19 · see citing works
Briefing: Stocktaking global warming: the outcomes of the 2023 Dubai Climate Summit (COP28)
Darby, S. J., Hammond, G. P., & Wu, J. (2024) · Proceedings of the Institution of Civil Engineers - Energy · doi:10.1680/jener.24.00005 · Cited by 19 · see citing works
Big Data in Climate Change Research: opportunities and Challenges
Mangal, P., Rajesh, A., & Misra, R. (2020) · International Conference on Intelligent Engineering and Management (ICIEM) · doi:10.1109/iciem48762.2020.9160174 · Cited by 19 · see citing works
“Earth is a intricate, dynamic system in never-ending motion to balance energy and sustain its livable conditions [2][3].”
BackgroundGuo, Z., Feng, C., Liu, Y., & Liu, Q. (2024) · Agricultural Water Management · doi:10.1016/j.agwat.2024.109157 · Cited by 22 · see citing works
Constantin, A., & Johnson, R. S. (2016) · Physics Letters A · doi:10.1016/j.physleta.2016.07.036 · Cited by 16 · see citing works
A quantitative and text-based characterization of big data research
Gupta, V., Singh, V. K., Ghose, U., & Mukhija, P. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-179016 · Cited by 16 · see citing works
“Var-ied solutions for solving real-time issues in big data have been given, for instance3D terrain modelling [43], climate change [44] etc. Big Data will continue to grow horizontally as well as vertically encompassing more and more na¨ive fields and in the time to come more and more technological…”
DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems
Rupe, A., Kumar, N., Epifanov, V., Kashinath, K., Pavlyk, O., Schlimbach, F., Patwary, M. A., Maidanov, S., Lee, V., Prabhat, M., et al. (2019) · Workshop on Machine Learning in HPC Environments · doi:10.1109/mlhpc49564.2019.00013 · Cited by 16 · see citing works
“training and generalization error) breaks down here [8] since TECA is not ground truth and we do not know how to train a DL model”
BackgroundPrior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity
Shavalieva, G., Papadokonstantakis, S., & Peters, G. M. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01079 · Cited by 15 · see citing works
“The collected knowledge(37) suggests a consensus among researchers that acute toxicity is defined by the mode of toxicological action and the chemical characteristics.(87) The higher toxicity values have often been associated with increased lipohilicity.(50,88,89) The most toxic compounds were hydrophobic and acted as…”
BackgroundMethodInfluentialBig Data Enters Environmental Law
Lajaunie, C., Schäfer, B., & Mazzéga, P. (2019) · Transnational Environmental Law · doi:10.1017/s2047102519000335 · Cited by 15 · see citing works
Theology, Sustainability and Big Data
Allam, Z. (2019) · Springer briefs in geography · doi:10.1007/978-3-030-29673-5_4 · Cited by 14 · see citing works
Thompson, S., Teixeira-Dias, F., Paulino, M., & Hamilton, A. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.07.034 · Cited by 14 · see citing works
Adebisi John, A., Babatunde, D. E., & Babatunde Olubayo, M. (2022) · Cogent Engineering · doi:10.1080/23311916.2022.2119537 · Cited by 14 · see citing works
“Faghmous and Kumar (2014) used a data science perspective to approach issues associated with climate change.”
BackgroundMolecular Techniques and Technologies in Biomonitoring for Environmental Sustainability
Hait, M., Patel, D., & Izah, S. C. (2024) · Springer eBooks · doi:10.1007/978-981-97-1658-6_17 · Cited by 15 · see citing works
Disease Surveillance System for Big Climate Data Processing and Dengue Transmission
Manogaran, G., & Lopez, D. (2018) · IGI Global eBooks · doi:10.4018/978-1-5225-5487-5.ch022 · Cited by 14 · see citing works
A theory-guided deep learning formulation of seismic waveform inversion
Sun, J., Niu, Z., Innanen, K. A., Li, J., & Trad, D. O. (2019) · Second International Meeting for Applied Geoscience & Energy · doi:10.1190/segam2019-3215762.1 · Cited by 14 · see citing works
“Theory guided data science has been outlined but not prescribed (Faghmous and Kumar, 2014; Karpatne et al., 2017, 2018), and while being largely in agreement with its tenets, the work of actually devising a theory-guided methodology must be done.”
BackgroundGraham, C. C. (2020) · Geoforum · doi:10.1016/j.geoforum.2020.07.004 · Cited by 14 · see citing works
Rao, N. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2979349 · Cited by 13 · see citing works
- Authors
- Faghmous, J. H., & Kumar, V.
- Affiliations
- University of Minnesota (US)
- Venue
- Big Data
- Publisher
- SAGE Publications
- Published
- 2014-08-25
- Volume
- 2
- Issue
- 3
- Pages
- 155-163
- ISSN
- 2167-6461 (ISSN-L)
- 2167-647X
- PMC
- PMC4174912
- OpenAlex
- W1980640510
- Semantic Scholar
- 9ba08d45d60130c7e5880f63a980b185a86e177c
- Cited by
- 223
- References
- 32
- Citations by year
- 2026: 10 · 2025: 12 · 2024: 29 · 2023: 25 · 2022: 32 · 2021: 22 · 2020: 14 · 2019: 28 · 2018: 19 · 2017: 13 · 2016: 9 · 2015: 9 · 2014: 1
- FWCI
- 19.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Big Data (publishedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (1355072, 1029711)
- University of Minnesota
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Social Sciences › Sociology and Political Science › Climate Change Communication and Perception
- Keywords
- climate change, climate data, climate science, data mining, scientific theories, data interpretation
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