scholarly journals The Role of Irrigation Expansion in Past and Future Temperature Trends

2008 ◽  
Vol 12 (3) ◽  
pp. 1-11 ◽  
Author(s):  
David B. Lobell ◽  
Céline Bonfils ◽  
Jean-Marc Faurès
Keyword(s):  
Greenhouse Gases ◽  
Climate Models ◽  
Climate Model ◽  
Masking Effect ◽  
Climate Trends ◽  
Local Cooling ◽  
Temperature Trends ◽  
Model Predictions ◽  
Cooling Effects ◽  
Climate Model Simulations

Abstract Expansion of irrigated land can cause local cooling of daytime temperatures by up to several degrees Celsius. Here the authors compare the expected cooling associated with rates of irrigation expansion in developing countries for historical (1961–2000) and future (2000–30) periods with climate model predictions of temperature changes from other forcings, most notably increased atmospheric greenhouse gas levels, over the same periods. Indirect effects of irrigation on climate, via methane production in paddy rice systems, were not considered. In regions of rapid irrigation growth over the past 40 yr, such as northwestern India and northeastern China, irrigation’s expected cooling effects have been similar in magnitude to climate model predictions of warming from greenhouse gases. A masking effect of irrigation can therefore explain the lack of significant increases in observed growing season maximum temperatures in these regions and the apparent discrepancy between observations and climate model simulations. Projections of irrigation for 2000–30 indicate a slowing of expansion rates, and therefore cooling from irrigation expansion over this time period will very likely be smaller than in recent decades. At the same time, warming from greenhouse gases will likely accelerate, and irrigation will play a relatively smaller role in agricultural climate trends. In many irrigated regions, therefore, temperature projections from climate models, which generally ignore irrigation, may be more accurate in predicting future temperature trends than their performance in reproducing past observed trends in irrigated regions would suggest.

scholarly journals The DeepMIP contribution to PMIP4: experimental design for model simulations of the EECO, PETM, and pre-PETM (version 1.0)

2017 ◽  
Vol 10 (2) ◽  
pp. 889-901 ◽  
Author(s):  
Daniel J. Lunt ◽  
Matthew Huber ◽  
Eleni Anagnostou ◽  
Michiel L. J. Baatsen ◽  
Rodrigo Caballero ◽  
...  
Keyword(s):  
Experimental Design ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Initial Conditions ◽  
Geological Data ◽  
Time Model ◽  
Model Simulations ◽  
Intercomparison Project ◽  
Climate Model Simulations

Abstract. Past warm periods provide an opportunity to evaluate climate models under extreme forcing scenarios, in particular high ( >  800 ppmv) atmospheric CO2 concentrations. Although a post hoc intercomparison of Eocene ( ∼  50  Ma) climate model simulations and geological data has been carried out previously, models of past high-CO2 periods have never been evaluated in a consistent framework. Here, we present an experimental design for climate model simulations of three warm periods within the early Eocene and the latest Paleocene (the EECO, PETM, and pre-PETM). Together with the CMIP6 pre-industrial control and abrupt 4 ×  CO2 simulations, and additional sensitivity studies, these form the first phase of DeepMIP – the Deep-time Model Intercomparison Project, itself a group within the wider Paleoclimate Modelling Intercomparison Project (PMIP). The experimental design specifies and provides guidance on boundary conditions associated with palaeogeography, greenhouse gases, astronomical configuration, solar constant, land surface processes, and aerosols. Initial conditions, simulation length, and output variables are also specified. Finally, we explain how the geological data sets, which will be used to evaluate the simulations, will be developed.

How to combine climate models and observations in event attribution?

2021 ◽  
Author(s):  
Yoann Robin ◽  
Aurélien Ribes
Keyword(s):  
Bayesian Method ◽  
Climate Models ◽  
Climate Model ◽  
Coupled Model ◽  
Model Data ◽  
Human Influence ◽  
Scale Parameters ◽  
Event Attribution ◽  
Intercomparison Project ◽  
Climate Model Simulations

<p>We describe a statistical method to derive event attribution diagnoses combining climate model simulations and observations. We fit nonstationary Generalized Extreme Value (GEV) distributions to extremely hot temperatures from an ensemble of Coupled Model Intercomparison Project phase 5 (CMIP)<br>models. In order to select a common statistical model, we discuss which GEV parameters have to be nonstationary and which do not. Our tests suggest that the location and scale parameters of GEV distributions should be considered nonstationary. Then, a multimodel distribution is constructed and constrained by observations using a Bayesian method. This new method is applied to the July 2019 French heatwave. Our results show that<br>both the probability and the intensity of that event have increased significantly in response to human influence.<br>Remarkably, we find that the heat wave considered might not have been possible without climate change. Our<br>results also suggest that combining model data with observations can improve the description of hot temperature<br>distribution.</p>

scholarly journals The DeepMIP contribution to PMIP4: methodologies for selection, compilation and analysis of latest Paleocene and early Eocene climate proxy data, incorporating version 0.1 of the DeepMIP database

2019 ◽  
Vol 12 (7) ◽  
pp. 3149-3206 ◽  
Author(s):  
Christopher J. Hollis ◽  
Tom Dunkley Jones ◽  
Eleni Anagnostou ◽  
Peter K. Bijl ◽  
Margot J. Cramwinckel ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Early Eocene ◽  
Proxy Data ◽  
Time Model ◽  
Deep Time ◽  
Climate Proxy ◽  
Early Eocene Climatic Optimum ◽  
Climate Model Simulations ◽  
Atmospheric Co2 Concentrations

Abstract. The early Eocene (56 to 48 million years ago) is inferred to have been the most recent time that Earth's atmospheric CO2 concentrations exceeded 1000 ppm. Global mean temperatures were also substantially warmer than those of the present day. As such, the study of early Eocene climate provides insight into how a super-warm Earth system behaves and offers an opportunity to evaluate climate models under conditions of high greenhouse gas forcing. The Deep Time Model Intercomparison Project (DeepMIP) is a systematic model–model and model–data intercomparison of three early Paleogene time slices: latest Paleocene, Paleocene–Eocene thermal maximum (PETM) and early Eocene climatic optimum (EECO). A previous article outlined the model experimental design for climate model simulations. In this article, we outline the methodologies to be used for the compilation and analysis of climate proxy data, primarily proxies for temperature and CO2. This paper establishes the protocols for a concerted and coordinated effort to compile the climate proxy records across a wide geographic range. The resulting climate “atlas” will be used to constrain and evaluate climate models for the three selected time intervals and provide insights into the mechanisms that control these warm climate states. We provide version 0.1 of this database, in anticipation that this will be expanded in subsequent publications.

scholarly journals Evaluating Climate Model Simulations of the Radiative Forcing and Radiative Response at Earth’s Surface

2019 ◽  
Vol 32 (13) ◽  
pp. 4089-4102 ◽  
Author(s):  
Ryan J. Kramer ◽  
Brian J. Soden ◽  
Angeline G. Pendergrass
Keyword(s):  
Radiative Forcing ◽  
Climate Models ◽  
Climate Model ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Global Climate Models ◽  
Lapse Rate ◽  
Radiation Budget ◽  
Model Simulations ◽  
Climate Model Simulations

Abstract We analyze the radiative forcing and radiative response at Earth’s surface, where perturbations in the radiation budget regulate the atmospheric hydrological cycle. By applying a radiative kernel-regression technique to CMIP5 climate model simulations where CO2 is instantaneously quadrupled, we evaluate the intermodel spread in surface instantaneous radiative forcing, radiative adjustments to this forcing, and radiative responses to surface warming. The cloud radiative adjustment to CO2 forcing and the temperature-mediated cloud radiative response exhibit significant intermodel spread. In contrast to its counterpart at the top of the atmosphere, the temperature-mediated cloud radiative response at the surface is found to be positive in some models and negative in others. Also, the compensation between the temperature-mediated lapse rate and water vapor radiative responses found in top-of-atmosphere calculations is not present for surface radiative flux changes. Instantaneous radiative forcing at the surface is rarely reported for model simulations; as a result, intermodel differences have not previously been evaluated in global climate models. We demonstrate that the instantaneous radiative forcing is the largest contributor to intermodel spread in effective radiative forcing at the surface. We also find evidence of differences in radiative parameterizations in current models and argue that this is a significant, but largely overlooked, source of bias in climate change simulations.

Assessing the Risk of Persistent Drought Using Climate Model Simulations and Paleoclimate Data

2014 ◽  
Vol 27 (20) ◽  
pp. 7529-7549 ◽  
Author(s):  
Toby R. Ault ◽  
Julia E. Cole ◽  
Jonathan T. Overpeck ◽  
Gregory T. Pederson ◽  
David M. Meko
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
State Of The Art ◽  
Global Climate ◽  
Global Climate Models ◽  
Mitigation Strategies ◽  
Drought Risk ◽  
Climate Model Simulations ◽  
Projected Changes ◽  
Paleoclimate Data

Abstract Projected changes in global rainfall patterns will likely alter water supplies and ecosystems in semiarid regions during the coming century. Instrumental and paleoclimate data indicate that natural hydroclimate fluctuations tend to be more energetic at low (multidecadal to multicentury) than at high (interannual) frequencies. State-of-the-art global climate models do not capture this characteristic of hydroclimate variability, suggesting that the models underestimate the risk of future persistent droughts. Methods are developed here for assessing the risk of such events in the coming century using climate model projections as well as observational (paleoclimate) information. Where instrumental and paleoclimate data are reliable, these methods may provide a more complete view of prolonged drought risk. In the U.S. Southwest, for instance, state-of-the-art climate model projections suggest the risk of a decade-scale megadrought in the coming century is less than 50%; the analysis herein suggests that the risk is at least 80%, and may be higher than 90% in certain areas. The likelihood of longer-lived events (>35 yr) is between 20% and 50%, and the risk of an unprecedented 50-yr megadrought is nonnegligible under the most severe warming scenario (5%–10%). These findings are important to consider as adaptation and mitigation strategies are developed to cope with regional impacts of climate change, where population growth is high and multidecadal megadrought—worse than anything seen during the last 2000 years—would pose unprecedented challenges to water resources in the region.

scholarly journals On the Relationship between Mean Monsoon Precipitation and Low Pressure Systems in Climate Model Simulations

2015 ◽  
Vol 28 (13) ◽  
pp. 5305-5324 ◽  
Author(s):  
V. Praveen ◽  
S. Sandeep ◽  
R. S. Ajayamohan
Keyword(s):  
Summer Monsoon ◽  
Climate Models ◽  
Climate Model ◽  
Low Pressure ◽  
Tracking Algorithm ◽  
Monsoon Precipitation ◽  
Model Simulations ◽  
Detection And Tracking ◽  
Climate Model Simulations ◽  
Low Pressure Systems

Abstract The north-northwest-propagating low pressure systems (LPS) are an important component of the Indian summer monsoon (ISM). The objective detection and tracking of LPS in reanalysis products and climate model simulations are challenging because of the weak structure of the LPS compared to tropical cyclones. Therefore, the skill of reanalyses and climate models in simulating the monsoon LPS is unknown. A robust method is presented here to objectively identify and track LPS, which mimics the conventional identification and tracking algorithm based on detecting closed isobars on surface pressure charts. The new LPS tracking technique allows a fair comparison between the observed and simulated LPS. The analysis based on the new tracking algorithm shows that the reanalyses from ERA-Interim and MERRA were able to reproduce the observed climatology and interannual variability of the monsoon LPS with a fair degree of accuracy. Further, the newly developed LPS detection and tracking algorithm is also applied to the climate model simulations of phase 5 of the Coupled Model Intercomparison Project (CMIP5). The CMIP5 models show considerable spread in terms of their skill in LPS simulation. About 60% of the observed total summer monsoon precipitation over east-central India is found to be associated with LPS activities, while in model simulations this ratio varies between 5% and 60%. Those models that simulate synoptic activity realistically are found to have better skill in simulating seasonal mean monsoon precipitation. The model-to-model variability in the simulated synoptic activity is found to be linked to the intermodel spread in zonal wind shear over the Indian region, which is further linked to inadequate representation of the tropical easterly jet in climate models. These findings elucidate the mechanisms behind the model simulation of ISM precipitation, synoptic activity, and their interdependence.

scholarly journals Increased variability in Greenland Ice Sheet runoff from satellite observations

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Thomas Slater ◽  
Andrew Shepherd ◽  
Malcolm McMillan ◽  
Amber Leeson ◽  
Lin Gilbert ◽  
...  
Keyword(s):  
Ocean Circulation ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Model Simulations ◽  
Climate Model Simulations

AbstractRunoff from the Greenland Ice Sheet has increased over recent decades affecting global sea level, regional ocean circulation, and coastal marine ecosystems, and it now accounts for most of the contemporary mass imbalance. Estimates of runoff are typically derived from regional climate models because satellite records have been limited to assessments of melting extent. Here, we use CryoSat-2 satellite altimetry to produce direct measurements of Greenland’s runoff variability, based on seasonal changes in the ice sheet’s surface elevation. Between 2011 and 2020, Greenland’s ablation zone thinned on average by 1.4 ± 0.4 m each summer and thickened by 0.9 ± 0.4 m each winter. By adjusting for the steady-state divergence of ice, we estimate that runoff was 357 ± 58 Gt/yr on average – in close agreement with regional climate model simulations (root mean square difference of 47 to 60 Gt/yr). As well as being 21 % higher between 2011 and 2020 than over the preceding three decades, runoff is now also 60 % more variable from year-to-year as a consequence of large-scale fluctuations in atmospheric circulation. Because this variability is not captured in global climate model simulations, our satellite record of runoff should help to refine them and improve confidence in their projections.

scholarly journals Improvement of climate predictions and reduction of their uncertainties using learning algorithms

2015 ◽  
Vol 15 (5) ◽  
pp. 7707-7734 ◽  
Author(s):  
E. Strobach ◽  
G. Bel
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Climate Dynamics ◽  
Past Performance ◽  
Decadal Time Scale ◽  
Simulated Climate ◽  
Climate Model Simulations ◽  
Decadal Climate

Abstract. Simulated climate dynamics, initialized with observed conditions is expected to be synchronized, for several years, with the actual dynamics. However, the predictions of climate models are not sufficiently accurate. Moreover, there is a large variance between simulations initialized at different times and between different models. One way to improve climate predictions and to reduce the associated uncertainties is to use an ensemble of climate model predictions, weighted according to their past performance. Here, we show that skillful predictions, for a decadal time scale, of the 2 m-temperature can be achieved by applying a sequential learning algorithm to an ensemble of decadal climate model simulations. The predictions generated by the learning algorithm are shown to be better than those of each of the models in the ensemble, the better performing simple average and a reference climatology. In addition, the uncertainties associated with the predictions are shown to be reduced relative to those derived from equally weighted ensemble of bias corrected predictions. The results show that learning algorithms can help to better assess future climate dynamics.

scholarly journals Reducing climate model biases by exploring parameter space with large ensembles of climate model simulations and statistical emulation

2019 ◽  
Vol 12 (7) ◽  
pp. 3017-3043 ◽  
Author(s):  
Sihan Li ◽  
David E. Rupp ◽  
Linnia Hawkins ◽  
Philip W. Mote ◽  
Doug McNeall ◽  
...  
Keyword(s):  
Parameter Space ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Atmospheric Model ◽  
Bias Reduction ◽  
Sensitivity Analyses ◽  
Model Framework ◽  
Model Simulations ◽  
Climate Model Simulations

Abstract. Understanding the unfolding challenges of climate change relies on climate models, many of which have large summer warm and dry biases over Northern Hemisphere continental midlatitudes. This work, with the example of the model used in the updated version of the weather@home distributed climate model framework, shows the potential for improving climate model simulations through a multiphased parameter refinement approach, particularly over the northwestern United States (NWUS). Each phase consists of (1) creating a perturbed parameter ensemble with the coupled global–regional atmospheric model, (2) building statistical emulators that estimate climate metrics as functions of parameter values, (3) and using the emulators to further refine the parameter space. The refinement process includes sensitivity analyses to identify the most influential parameters for various model output metrics; results are then used to cull parameters with little influence. Three phases of this iterative process are carried out before the results are considered to be satisfactory; that is, a handful of parameter sets are identified that meet acceptable bias reduction criteria. Results not only indicate that 74 % of the NWUS regional warm biases can be reduced by refining global atmospheric parameters that control convection and hydrometeor transport, as well as land surface parameters that affect plant photosynthesis, transpiration, and evaporation, but also suggest that this iterative approach to perturbed parameters has an important role to play in the evolution of physical parameterizations.

scholarly journals Improving climate model accuracy by exploring parameter space with an O(10<sup>5</sup>) member ensemble and emulator

2018 ◽  
Author(s):  
Sihan Li ◽  
David E. Rupp ◽  
Linnia Hawkins ◽  
Philip W. Mote ◽  
Doug McNeall ◽  
...  
Keyword(s):  
Parameter Space ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Atmospheric Model ◽  
Bias Reduction ◽  
Sensitivity Analyses ◽  
Model Framework ◽  
Climate Model Simulations ◽  
Perturbed Physics

Abstract. Understanding the unfolding challenges of climate change relies on climate models, many of which have large summer warm and dry biases over Northern Hemisphere continental mid-latitudes. This work, using the example of the model used in the updated version of the weather@home distributed climate model framework, shows the potential for improving climate model simulations through a multi-phased parameter refinement approach, particularly over northwestern United States(NWUS). Each phase consists of 1) creating a perturbed physics ensemble with the coupled global – regional atmospheric model, 2) building statistical emulators that estimate climate metrics as functions of parameter values, 3) and using the emulators to further refine the parameter space. The refinement process includes sensitivity analyses to identify the most influential parameters for various model output metrics; results are then used to cull parameters with little influence. Three phases of this iterative process are carried out before the results are considered to be satisfactory; that is, a handful of parameter sets are identified that meet acceptable bias reduction criteria. Results not only indicate that 74 % of the NWUS regional warm biases can be reduced by refining global atmospheric parameters that control convection and hydrometeor transport, and land surface parameters that affect plant photosynthesis, transpiration and evaporation, but also suggest that this iterative approach to perturbed physics has an important role to play in the evolution of physical parameterizations.

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