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Journal articles on the topic 'Climate Dependence'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Reusen, Jesse, Eveline van der Linden, and Richard Bintanja. "Differences between Arctic Interannual and Decadal Variability across Climate States." Journal of Climate 32, no. 18 (2019): 6035–50. http://dx.doi.org/10.1175/jcli-d-18-0672.1.

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ABSTRACTLong-term climate variations have the potential to amplify or dampen (human-induced) trends in temperature. Understanding natural climate variability is therefore of vital importance, especially since the variability itself may change with a changing climate. Here, we quantify the magnitude and other characteristics of interannual to decadal variability in Arctic temperature and their dependence on the climate state. Moreover, we identify the processes responsible for the state dependency of the variations, using five quasi-equilibrium climate simulations of a state-of-the-art global c

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Geoffroy, Olivier, and David Saint-Martin. "Equilibrium- and Transient-State Dependencies of Climate Sensitivity: Are They Important for Climate Projections?" Journal of Climate 33, no. 5 (2020): 1863–79. http://dx.doi.org/10.1175/jcli-d-19-0248.1.

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AbstractThe effective equilibrium climate sensitivity is generally assumed to be constant in climate change studies, whereas it may vary due to different mechanisms. This study assesses the importance of the different types of state dependencies of the radiative feedbacks for constraining climate projections from the historical record. In transition, the radiative feedbacks may vary with the changes in the warming pattern due to inhomogeneous ocean heat uptake. They may also vary in equilibrium due to their dependence on both temperature and CO2 concentration. A two-layer energy balance model

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Ishizaki, Yasuhiro, Hideo Shiogama, Seita Emori, et al. "Dependence of Precipitation Scaling Patterns on Emission Scenarios for Representative Concentration Pathways." Journal of Climate 26, no. 22 (2013): 8868–79. http://dx.doi.org/10.1175/jcli-d-12-00540.1.

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Abstract Pattern scaling is an efficient way to generate projections of regional climate change for various emission scenarios. This approach assumes that the spatial pattern of changes per degree of global warming (scaling pattern) is the same among emission scenarios. The hypothesis was tested for the scaling pattern of precipitation by focusing on the scenario dependence of aerosol scaling patterns. The scenario dependence of aerosol scaling patterns induced the scenario dependence of the surface shortwave radiation scaling pattern. The scenario dependence of the surface shortwave radiation

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Nurhayati, Nurhayati, Muhammad Rozzaq Hamidi, Utriweni Mukhaiyar, and Kurnia Novita Sari. "Cross-Correlation Analysis in Evaluating Spatio-Temporal Data Dependence of Climate Variables Through the GSTAR Model." Jurnal Matematika, Statistika dan Komputasi 21, no. 3 (2025): 813–31. https://doi.org/10.20956/j.v21i3.43665.

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Climate change analysis requires an approach which is capable to accommodate the dynamics of relationships between climatological variables in space and time dimensions. Temperature, humidity, and rainfall vary temporally and exhibit spatial dependence across locations. This study applies the Generalized Space-Time Autoregressive (GSTAR) model to analyze the spatial and temporal dependence patterns of climate variables in Tasikmalaya. The novelty of this study lies in the cross-correlation analysis of climate variables using actual data and model estimation results. This analysis can be used t

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Ahluwalia, Pal, and Toby Miller. "Aid, dependence, climate—a pacific dilemma." Social Identities 27, no. 1 (2021): 1–2. http://dx.doi.org/10.1080/13504630.2020.1869226.

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6

Senior, Catherine A., and John F. B. Mitchell. "The time-dependence of climate sensitivity." Geophysical Research Letters 27, no. 17 (2000): 2685–88. http://dx.doi.org/10.1029/2000gl011373.

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7

Fischer, Matt J. "Investigating Nonlinear Dependence between Climate Fields." Journal of Climate 30, no. 14 (2017): 5547–62. http://dx.doi.org/10.1175/jcli-d-16-0563.1.

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The Earth’s ice–ocean–atmosphere system exhibits nonlinear responses, such as the difference in the magnitude of the atmospheric response to positive or negative ocean or sea ice anomalies. Two classes of methods that have previously been used to investigate the nonlinear dependence between climate fields are kernel methods and neural network methods. In this paper, a third methodology is introduced: gradient-based kernel dimension reduction. Gradient-based kernel methods are an extension of conventional kernel methods, but gradient-based methods focus on the directional derivatives of the reg

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8

HANSEN, J., G. RUSSELL, A. LACIS, I. FUNG, D. RIND, and P. STONE. "Climate Response Times: Dependence on Climate Sensitivity and Ocean Mixing." Science 229, no. 4716 (1985): 857–59. http://dx.doi.org/10.1126/science.229.4716.857.

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9

Hao, Zengchao, and Vijay P. Singh. "Review of dependence modeling in hydrology and water resources." Progress in Physical Geography: Earth and Environment 40, no. 4 (2016): 549–78. http://dx.doi.org/10.1177/0309133316632460.

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Various methods have been developed over the past five decades for dependence modeling of multivariate variables in hydrology and water resources, but there has been no overall review of techniques commonly used in the field. This paper, therefore, introduces several methods focusing on dependence structure modeling, including parametric distribution, entropy, copula, and nonparametric. Recent advances in modeling dependences mainly reside in nonlinear dependence modeling (including extreme dependence) with flexible marginal distributions, and in high-dimension dependence modeling via the vine

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10

Abramowitz, G., and C. H. Bishop. "Climate Model Dependence and the Ensemble Dependence Transformation of CMIP Projections." Journal of Climate 28, no. 6 (2015): 2332–48. http://dx.doi.org/10.1175/jcli-d-14-00364.1.

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Abstract Obtaining multiple estimates of future climate for a given emissions scenario is key to understanding the likelihood and uncertainty associated with climate-related impacts. This is typically done by collating model estimates from different research institutions internationally with the assumption that they constitute independent samples. Heuristically, however, several factors undermine this assumption: shared treatment of processes between models, shared observed data for evaluation, and even shared model code. Here, a “perfect model” approach is used to test whether a previously pr

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11

Rind, David. "Dependence of Warm and Cold Climate Depiction on Climate Model Resolution." Journal of Climate 1, no. 10 (1988): 965–97. http://dx.doi.org/10.1175/1520-0442(1988)001<0965:dowacc>2.0.co;2.

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12

Vrac, Mathieu. "Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R<sup>2</sup>D<sup>2</sup>) bias correction." Hydrology and Earth System Sciences 22, no. 6 (2018): 3175–96. http://dx.doi.org/10.5194/hess-22-3175-2018.

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Abstract. Climate simulations often suffer from statistical biases with respect to observations or reanalyses. It is therefore common to correct (or adjust) those simulations before using them as inputs into impact models. However, most bias correction (BC) methods are univariate and so do not account for the statistical dependences linking the different locations and/or physical variables of interest. In addition, they are often deterministic, and stochasticity is frequently needed to investigate climate uncertainty and to add constrained randomness to climate simulations that do not possess

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13

Kutzbach, John E., Feng He, Steve J. Vavrus, and William F. Ruddiman. "The dependence of equilibrium climate sensitivity on climate state: Applications to studies of climates colder than present." Geophysical Research Letters 40, no. 14 (2013): 3721–26. http://dx.doi.org/10.1002/grl.50724.

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14

Beseoglu, Kerim, Daniel Hänggi, Walter Stummer, and Hans-Jakob Steiger. "DEPENDENCE OF SUBARACHNOID HEMORRHAGE ON CLIMATE CONDITIONS." Neurosurgery 62, no. 5 (2008): 1033–39. http://dx.doi.org/10.1227/01.neu.0000325864.91584.c7.

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15

Andrew, Robbie M., Steven J. Davis, and Glen P. Peters. "Climate policy and dependence on traded carbon." Environmental Research Letters 8, no. 3 (2013): 034011. http://dx.doi.org/10.1088/1748-9326/8/3/034011.

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16

Emanuel, Kerry A. "The dependence of hurricane intensity on climate." Nature 326, no. 6112 (1987): 483–85. http://dx.doi.org/10.1038/326483a0.

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17

Kuma, Peter, Frida A.-M. Bender, and Aiden R. Jönsson. "Climate model code genealogy and its relation to climate feedbacks and sensitivity." Journal of Advances in Modeling of Earth Systems 15, no. 7 (2023): e2022MS003588. https://doi.org/10.5281/zenodo.8140164.

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Contemporary general circulation models (GCMs) and Earth system models (ESMs) are developed by a large number of modeling groups globally. They use a wide range of representations of physical processes, allowing for structural (code) uncertainty to be partially quantified with multi-model ensembles (MMEs). Many models in the MMEs of the Coupled Model Intercomparison Project (CMIP) have a common development history due to sharing of code and schemes. This makes their projections statistically dependent and introduces biases in MME statistics. Previous research has focused on model output and co

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18

Anghel, Bogdan Ionut, and Radu Lupu. "The Role of Climate Change News in Pharma Industry." Proceedings of the International Conference on Business Excellence 17, no. 1 (2023): 19–28. http://dx.doi.org/10.2478/picbe-2023-0004.

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Abstract In this article, we aim to study the role of climate change news in the European Big-Pharma companies returns. The goal of the research is to explain if the sentiment of the news posted on Twitter can explain the market movements, even if current global events may affect stock prices. The previous literature reveals the impact of climate change in the economy, but in the field of pharma companies, there is less information of how climate change affects the evolution in the stock market. We used the VADER methodology to examine and analyze the climate change news posted on Twitter. Thi

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19

Hnilica, Jan, Martin Hanel, and Vladimír Puš. "Technical note: Changes in cross- and auto-dependence structures in climate projections of daily precipitation and their sensitivity to outliers." Hydrology and Earth System Sciences 23, no. 3 (2019): 1741–49. http://dx.doi.org/10.5194/hess-23-1741-2019.

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Abstract. Simulations of regional or global climate models are often used for climate change impact assessment. To eliminate systematic errors, which are inherent to all climate model simulations, a number of post-processing (statistical downscaling) methods have been proposed recently. In addition to basic statistical properties of simulated variables, some of these methods also consider a dependence structure between or within variables. In the present paper we assess the changes in cross- and auto-correlation structures of daily precipitation in six regional climate model simulations. In ad

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20

Luceño, Alberto, Melisa Menéndez, and Fernando J. Méndez. "The effect of temporal dependence on the estimation of the frequency of extreme ocean climate events." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 462, no. 2070 (2006): 1683–97. http://dx.doi.org/10.1098/rspa.2005.1652.

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The term ‘extreme ocean climate estimation’ refers to the assessment of the statistical distribution of extreme oceanographical geophysical variables. Components of the ocean climate are variables, such as the storm surge, wind velocity and significant wave height. Important characteristics of extreme ocean climate are the frequencies of the exceedances of ocean climate variables over selected thresholds. Assuming that exceedances are statistically independent of each other, their frequencies can be estimated using non-homogeneous Poisson processes. However, exceedances often exhibit temporal

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21

Krinner, G., and C. Genthon. "Altitude dependence of the ice sheet surface climate." Geophysical Research Letters 26, no. 15 (1999): 2227–30. http://dx.doi.org/10.1029/1999gl900536.

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22

Solomon, Stanley C., Han‐Li Liu, Daniel R. Marsh, Joseph M. McInerney, Liying Qian, and Francis M. Vitt. "Whole Atmosphere Climate Change: Dependence on Solar Activity." Journal of Geophysical Research: Space Physics 124, no. 5 (2019): 3799–809. http://dx.doi.org/10.1029/2019ja026678.

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23

Grundmann, Reiner. "Ozone and climate governance: An implausible path dependence." Comptes Rendus Geoscience 350, no. 7 (2018): 435–41. http://dx.doi.org/10.1016/j.crte.2018.07.008.

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24

Bishop, Craig H., and Gab Abramowitz. "Climate model dependence and the replicate Earth paradigm." Climate Dynamics 41, no. 3-4 (2012): 885–900. http://dx.doi.org/10.1007/s00382-012-1610-y.

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25

Zaliapin, I., and M. Ghil. "Another look at climate sensitivity." Nonlinear Processes in Geophysics 17, no. 2 (2010): 113–22. http://dx.doi.org/10.5194/npg-17-113-2010.

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Abstract. We revisit a recent claim that the Earth's climate system is characterized by sensitive dependence to parameters; in particular, that the system exhibits an asymmetric, large-amplitude response to normally distributed feedback forcing. Such a response would imply irreducible uncertainty in climate change predictions and thus have notable implications for climate science and climate-related policy making. We show that equilibrium climate sensitivity in all generality does not support such an intrinsic indeterminacy; the latter appears only in essentially linear systems. The main flaw

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26

Shea, Yolanda L., Bruce A. Wielicki, Sunny Sun-Mack, and Patrick Minnis. "Quantifying the Dependence of Satellite Cloud Retrievals on Instrument Uncertainty." Journal of Climate 30, no. 17 (2017): 6959–76. http://dx.doi.org/10.1175/jcli-d-16-0429.1.

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Cloud response to Earth’s changing climate is one of the largest sources of uncertainty among global climate model (GCM) projections. Two of the largest sources of uncertainty are the spread in equilibrium climate sensitivity (ECS) and uncertainty in radiative forcing due to uncertainty in the aerosol indirect effect. Satellite instruments with sufficient accuracy and on-orbit stability to detect climate change–scale trends in cloud properties will improve confidence in the understanding of the relationship between observed climate change and cloud property trends, thus providing information t

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27

Hein, Catherine L., Gunnar Öhlund, and Göran Englund. "Fish introductions reveal the temperature dependence of species interactions." Proceedings of the Royal Society B: Biological Sciences 281, no. 1775 (2014): 20132641. http://dx.doi.org/10.1098/rspb.2013.2641.

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A major area of current research is to understand how climate change will impact species interactions and ultimately biodiversity. A variety of environmental conditions are rapidly changing owing to climate warming, and these conditions often affect both the strength and outcome of species interactions. We used fish distributions and replicated fish introductions to investigate environmental conditions influencing the coexistence of two fishes in Swedish lakes: brown trout ( Salmo trutta ) and pike ( Esox lucius ). A logistic regression model of brown trout and pike coexistence showed that the

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28

Zhang, Huan, Klaus Fraedrich, Richard Blender, and Xiuhua Zhu. "Precipitation Extremes in CMIP5 Simulations on Different Time Scales." Journal of Hydrometeorology 14, no. 3 (2013): 923–28. http://dx.doi.org/10.1175/jhm-d-12-0181.1.

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Abstract Precipitation maxima in global climate model (GCM) simulations are compared with observations in terms of resolution dependence and climate change. The analysis shows the following results: (i) the observed scaling law relating precipitation maxima to duration is basically reproduced but exhibits resolution dependence, (ii) the intensity of precipitation extremes is up to one order of magnitude smaller in the model data, and (iii) the increase of precipitation maxima on short time scales in the warmer climate simulations [representative concentration pathway 8.5 (RCP8.5)] vanishes for

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29

Mangat, Manveer Kaur, and Erhard Reschenhofer. "Frequency-Domain Evidence for Climate Change." Econometrics 8, no. 3 (2020): 28. http://dx.doi.org/10.3390/econometrics8030028.

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The goal of this paper is to search for conclusive evidence against the stationarity of the global air surface temperature, which is one of the most important indicators of climate change. For this purpose, possible long-range dependencies are investigated in the frequency-domain. Since conventional tests of hypotheses about the memory parameter, which measures the degree of long-range dependence, are typically based on asymptotic arguments and are therefore of limited practical value in case of small or medium sample sizes, we employ a new small-sample test as well as a related estimator for

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30

Fretheim, Kjetil. "Oil Dependence and Climate Change: Public Theology in Norway." International Journal of Public Theology 10, no. 2 (2016): 193–210. http://dx.doi.org/10.1163/15697320-12341442.

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Petroleum production and consumption are not only a source of wealth and welfare, but also a root cause of contemporary climate change. This article deals with the cultural role of petroleum in contemporary globalized societies and how public theology can address the relationship between oil and climate change. I will examine how the Church of Norway is responding to Norwegian petroleum production and climate change and how the church understands the cultural role of petroleum production. I also discuss the moral, political and ecological challenges that follow from petroleum production and co

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31

Rogacheva, Olga, and Sergei Tolstykh. "Monitoring of the Central Bank of Russia: Analysis of Indicators." System Analysis & Mathematical Modeling 6, no. 3 (2024): 312–29. https://doi.org/10.17150/2713-1734.2024.6(3).312-329.

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One of the main tasks of statistics is to build socio-economic indicators to assess the state of phenomena and processes occurring in society. Banking monitoring of non-financial enterprises is based on the construction and analysis of business climate indicators. To date, in Russia as a whole, monitoring covers more than 14 thousand enterprises of both large and medium-sized and small enterprises in industry, agriculture, construction, transportation and storage, trade, and the service sector. According to the monitoring data, the Bank of Russia's business climate indicator is being built, wh

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32

Gedalin, M., and M. Balikhin. "Climate of utopia." Nonlinear Processes in Geophysics 15, no. 4 (2008): 541–49. http://dx.doi.org/10.5194/npg-15-541-2008.

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Abstract. Climate is usually considered to depend on a large number of parameters, this being essentially a functional in multi-dimensional parameter space. We propose a low-dimensional model of a climate where the temperature field on a thermally conducting planet depends on the external energy input and very limited number of internal parameters, like thermal conductivity and reflectivity. Equilibrium temperature and quasistatic variations of climate following slow variations of the energy input are studied. The single phase model exhibits adiabatic behavior and stability with respect to sma

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33

Ide, Tobias. "Recession and fossil fuel dependence undermine climate policy commitments." Environmental Research Communications 2, no. 10 (2020): 101002. http://dx.doi.org/10.1088/2515-7620/abbb27.

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34

Knox-Hayes, Janelle. "Negotiating climate legislation: Policy path dependence and coalition stabilization." Regulation & Governance 6, no. 4 (2012): 545–67. http://dx.doi.org/10.1111/j.1748-5991.2012.01138.x.

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35

Platt, C. Martin R., and Harshvardhan. "Temperature dependence of cirrus extinction: Implications for climate feedback." Journal of Geophysical Research 93, no. D9 (1988): 11051. http://dx.doi.org/10.1029/jd093id09p11051.

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36

Toberman, Hannah, Edward Tipping, John F. Boyle, Rachel C. Helliwell, Allan Lilly, and Peter A. Henrys. "Dependence of ombrotrophic peat nitrogen on phosphorus and climate." Biogeochemistry 125, no. 1 (2015): 11–20. http://dx.doi.org/10.1007/s10533-015-0117-0.

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37

Schmittner, Andreas, and Andrew J. Weaver. "Dependence of multiple climate states on ocean mixing parameters." Geophysical Research Letters 28, no. 6 (2001): 1027–30. http://dx.doi.org/10.1029/2000gl012410.

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38

Cunnington, W. M., and J. F. B. Mitchell. "On the dependence of climate sensitivity on convective parametrization." Climate Dynamics 4, no. 2 (1990): 85–93. http://dx.doi.org/10.1007/bf00208904.

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39

Zubillaga, María, Oscar Skewes, Nicolás Soto, and Jorge E. Rabinovich. "How density-dependence and climate affect guanaco population dynamics." Ecological Modelling 385 (October 2018): 189–96. http://dx.doi.org/10.1016/j.ecolmodel.2018.07.010.

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40

Mitchell, J. F. B., C. A. Wilson, and W. M. Cunnington. "On Co2 climate sensitivity and model dependence of results." Quarterly Journal of the Royal Meteorological Society 113, no. 475 (2007): 293–322. http://dx.doi.org/10.1002/qj.49711347517.

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41

Mohan, Preeya, and Diego Morris. "Climate finance and innovation with natural resource dependence thresholds." Journal of Environmental Management 377 (March 2025): 124683. https://doi.org/10.1016/j.jenvman.2025.124683.

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42

KAUFMANN, Christof. "THE PROBLEM OF THE DEPENDENCE OF GLOBAL WARMING ON THE INCREASE IN CO2 AS A SCIENTIFIC DEBATE." INNOVATIONS IN THE SCIENTIFIC, TECHNICAL AND SOCIAL ECOSYSTEMS 1, no. 5 (2023): 75–80. http://dx.doi.org/10.56378/chas020230301.

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The Purpose of the Study is theoretical substantiation of the problem of global warming and climate change. The Research Methodology. Methods of analysis, modelling, description and generalization of data were used to study the problem of the dependence of global warming and climate change on the increase in CO2. The Scientific Novelty of the study consists in identifying the contradictions of scientific conclusions regarding the direct dependence of global warming and climate change on the level of CO2, outlining new directions of experimental work on the specified problem. The Conclusion. Fo

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43

Onchere, Walter Omonywa. "Kenya Climate Change Interdependence Modelling using Archimedean Copulas." African Journal of Applied Statistics 11, no. 1 (2024): 1571–34. https://doi.org/10.16929/ajas/2024.1517.280.

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Using Kenya historical rainfall and temperature climate data from 1991 to 2016, we conduct an analysis of the interdependence structure for climate change risk analysis. The Archimedean copula approach to dependence modelling has been used by various researchers in modelling interdependent rates. In the present study, we apply the Archimedean copulas to address interdependence effects on climate change data from a developing country. Our research encompasses several key objectives: identifying the interdependence structure of rainfall and temperature data, construction of Clayton, Frank and Gu

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Higgins, Paul A. T., and Jonah V. Steinbuck. "A Conceptual Tool for Climate Change Risk Assessment." Earth Interactions 18, no. 21 (2014): 1–15. http://dx.doi.org/10.1175/ei-d-14-0003.1.

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Abstract This study develops a new conceptual tool to explore the potential societal consequences of climate change. The conceptual tool delineates three quasi-independent factors that contribute to the societal consequences of climate change: how climate changes; the sensitivity of physical systems, biological resources, and social institutions to climate change; and the degree of human dependence on those systems, resources, and institutions. This conceptual tool, as currently developed, is not predictive, but it enables the exploration of the dependence of climate change risks on key contri

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François, Bastien, and Mathieu Vrac. "Time of emergence of compound events: contribution of univariate and dependence properties." Natural Hazards and Earth System Sciences 23, no. 1 (2023): 21–44. http://dx.doi.org/10.5194/nhess-23-21-2023.

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Abstract. Many climate-related disasters often result from a combination of several climate phenomena, also referred to as “compound events’’ (CEs). By interacting with each other, these phenomena can lead to huge environmental and societal impacts, at a scale potentially far greater than any of these climate events could have caused separately. Marginal and dependence properties of the climate phenomena forming the CEs are key statistical properties characterising their probabilities of occurrence. In this study, we propose a new methodology to assess the time of emergence of CE probabilities

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46

Gainous, Jason, and Melissa K. Merry. "Climate Change as a National Security Issue: Examining Framing Effects Across Party." American Politics Research 50, no. 2 (2021): 199–212. http://dx.doi.org/10.1177/1532673x211053213.

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Research suggests that framing climate change as a national security issue can shape opinion about climate change. This research is less clear about what exactly constitutes a “national security frame” and what aspects of this frame are most persuasive. We use a survey experiment to compare the relative effects of three types of national security frames we identify. Results show that a frame centered on energy dependence had the strongest effect and was the most consistent across partisanship. Surprisingly, the effects ran in the opposite direction for Democrats and Republicans on both outcome

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Zhao, Yingying, Haijun Yang, and Zhengyu Liu. "Assessing Bjerknes Compensation for Climate Variability and Its Time-Scale Dependence." Journal of Climate 29, no. 15 (2016): 5501–12. http://dx.doi.org/10.1175/jcli-d-15-0883.1.

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Abstract The Bjerknes compensation (BJC) refers to the tendency for changes in the atmosphere heat transport (AHT) and ocean heat transport (OHT) to compensate each other. However, the nature of this compensation varies with the time scale of changes. In this study, a new approach was developed to diagnose BJC for climate variability by considering the correlation between AHT and OHT and their relative magnitudes. The correlation is equivalent to the cosine of phase difference between AHT and OHT. For high-frequency climate variability, AHT lags or leads OHT by π/2, the correlation is zero, an

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Lenderink, Geert, Nikolina Ban, Erwan Brisson, et al. "Are dependencies of extreme rainfall on humidity more reliable in convection-permitting climate models?" Hydrology and Earth System Sciences 29, no. 4 (2025): 1201–20. https://doi.org/10.5194/hess-29-1201-2025.

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Abstract. Convection-permitting climate models (CPMs) are becoming increasingly used in climate change studies. These models show greatly improved convective rainfall statistics compared to parameterized-convection regional climate models (RCMs), but are they also more reliable in a climate change setting? Increases in rainfall extremes are generally considered to be caused by increases in absolute humidity, primarily following from the Clausius–Clapeyron relation, while the influence of relative humidity changes is uncertain and not systematically explored. Quantifying these humidity dependen

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Schepen, Andrew, Yvette Everingham, and Quan J. Wang. "On the Joint Calibration of Multivariate Seasonal Climate Forecasts from GCMs." Monthly Weather Review 148, no. 1 (2020): 437–56. http://dx.doi.org/10.1175/mwr-d-19-0046.1.

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Abstract Multivariate seasonal climate forecasts are increasingly required for quantitative modeling in support of natural resources management and agriculture. GCM forecasts typically require postprocessing to reduce biases and improve reliability; however, current seasonal postprocessing methods often ignore multivariate dependence. In low-dimensional settings, fully parametric methods may sufficiently model intervariable covariance. On the other hand, empirical ensemble reordering techniques can inject desired multivariate dependence in ensembles from template data after univariate postproc

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Masson, David, and Reto Knutti. "Spatial-Scale Dependence of Climate Model Performance in the CMIP3 Ensemble." Journal of Climate 24, no. 11 (2011): 2680–92. http://dx.doi.org/10.1175/2011jcli3513.1.

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Abstract About 20 global climate models have been run for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) to predict climate change due to anthropogenic activities. Evaluating these models is an important step to establish confidence in climate projections. Model evaluation, however, is often performed on a gridpoint basis despite the fact that models are known to often be unreliable at such small spatial scales. In this study, the annual mean values of surface air temperature and precipitation are analyzed. Using a spatial smoothing technique with a variabl

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