scholarly journals Diagnosis of Multiyear Predictability on Continental Scales

2011 ◽  
Vol 24 (19) ◽  
pp. 5108-5124 ◽  
Author(s):  
Liwei Jia ◽  
Timothy DelSole
Keyword(s):  
Time Scales ◽  
Air Temperature ◽  
Climate Models ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Optimization Method ◽  
First Year ◽  
Temperature And Precipitation ◽  
Regional Prediction ◽  
Intercomparison Project

A new statistical optimization method is used to identify components of surface air temperature and precipitation on six continents that are predictable in multiple climate models on multiyear time scales. The components are identified from unforced “control runs” of the Coupled Model Intercomparison Project phase 3 dataset. The leading predictable components can be calculated in independent control runs with statistically significant skill for 3–6 yr for surface air temperature and 1–3 yr for precipitation, depending on the continent, using a linear regression model with global sea surface temperature (SST) as a predictor. Typically, lag-correlation maps reveal that the leading predictable components of surface air temperature are related to two types of SST patterns: persistent patterns near the continent itself and an oscillatory ENSO-like pattern. The only exception is Europe, which has no significant ENSO relation. The leading predictable components of precipitation are significantly correlated with an ENSO-like SST pattern. No multiyear predictability of land precipitation could be verified in Europe. The squared multiple correlations of surface air temperature and precipitation for nonzero lags on each continent are less than 0.4 in the first year, implying that less than 40% of variations of the leading predictable component can be predicted from global SST. The predictable components describe the spatial structures that can be predicted on multiyear time scales in the absence of anthropogenic and natural forcing, and thus provide a scientific rationale for regional prediction on multiyear time scales.

scholarly journals Evaluation of CMIP5 Global Climate Models for Simulating Climatological Temperature and Precipitation for Southeast Asia

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Suchada Kamworapan ◽  
Chinnawat Surussavadee
Keyword(s):  
Southeast Asia ◽  
Climate Models ◽  
Performance Metrics ◽  
Global Climate ◽  
Total Error ◽  
Coupled Model ◽  
Global Climate Models ◽  
Ensemble Average ◽  
Temperature And Precipitation ◽  
Intercomparison Project

This study evaluates the performances of all forty different global climate models (GCMs) that participate in the Coupled Model Intercomparison Project Phase 5 (CMIP5) for simulating climatological temperature and precipitation for Southeast Asia. Historical simulations of climatological temperature and precipitation of the 40 GCMs for the 40-year period of 1960–1999 for both land and sea and those for the century of 1901–1999 for land are evaluated using observation and reanalysis datasets. Nineteen different performance metrics are employed. The results show that the performances of different GCMs vary greatly. CNRM-CM5-2 performs best among the 40 GCMs, where its total error is 3.25 times less than that of GCM performing worst. The performance of CNRM-CM5-2 is compared with those of the ensemble average of all 40 GCMs (40-GCM-Ensemble) and the ensemble average of the 6 best GCMs (6-GCM-Ensemble) for four categories, i.e., temperature only, precipitation only, land only, and sea only. While 40-GCM-Ensemble performs best for temperature, 6-GCM-Ensemble performs best for precipitation. 6-GCM-Ensemble performs best for temperature and precipitation simulations over sea, whereas CNRM-CM5-2 performs best over land. Overall results show that 6-GCM-Ensemble performs best and is followed by CNRM-CM5-2 and 40-GCM-Ensemble, respectively. The total errors of 6-GCM-Ensemble, CNRM-CM5-2, and 40-GCM-Ensemble are 11.84, 13.69, and 14.09, respectively. 6-GCM-Ensemble and CNRM-CM5-2 agree well with observations and can provide useful climate simulations for Southeast Asia. This suggests the use of 6-GCM-Ensemble and CNRM-CM5-2 for climate studies and projections for Southeast Asia.

scholarly journals Reduced complexity model intercomparison project phase 1: Protocol, results and initial observations

2020 ◽  
Author(s):  
Zebedee R. J. Nicholls ◽  
Malte Meinshausen ◽  
Jared Lewis ◽  
Robert Gieseke ◽  
Dietmar Dommenget ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Climate Models ◽  
Phase 1 ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Internal Variability ◽  
Second Phase ◽  
Model Intercomparison ◽  
Intercomparison Project ◽  
Reduced Complexity

Abstract. Here we present results from the first phase of the Reduced Complexity Model Intercomparison Project (RCMIP). RCMIP is a systematic examination of reduced complexity climate models (RCMs), which are used to complement and extend the insights from more complex Earth System Models (ESMs), in particular those participating in the Sixth Coupled Model Intercomparison Project (CMIP6). In Phase 1 of RCMIP, with 14 participating models namely ACC2, AR5IR (2 and 3 box versions), CICERO-SCM, ESCIMO, FaIR, GIR, GREB, Hector, Held et al. two layer model, MAGICC, MCE, OSCAR and WASP, we highlight the structural differences across various RCMs and show that RCMs are capable of reproducing global-mean surface air temperature (GSAT) changes of ESMs and historical observations. We find that some RCMs are capable of emulating the GSAT response of CMIP6 models to within a root-mean square error of 0.2 °C (of the same order of magnitude as ESM internal variability) over a range of scenarios. Running the same model configurations for both RCP and SSP scenarios, we see that the SSPs exhibit higher effective radiative forcing throughout the second half of the 21st Century. Comparing our results to the difference between CMIP5 and CMIP6 output, we find that the change in scenario explains approximately 46 % of the increase in higher end projected warming between CMIP5 and CMIP6. This suggests that changes in ESMs from CMIP5 to CMIP6 explain the rest of the increase, hence the higher climate sensitivities of available CMIP6 models may not be having as large an impact on GSAT projections as first anticipated. A second phase of RCMIP will complement RCMIP Phase 1 by exploring probabilistic results and emulation in more depth to provide results available for the IPCC's Sixth Assessment Report author teams.

scholarly journals Climate Projections Over Different Climatic Regions of Afghanistan for Shared Socioeconomic Scenarios

2022 ◽  
Author(s):  
Mohammad Naser Sediqi ◽  
Vempi Satriya Adi Hendrawan ◽  
Daisuke Komori
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Global Climate Models ◽  
Maximum Temperature ◽  
Climate Projections ◽  
Climatic Regions ◽  
Temperature And Precipitation ◽  
Intercomparison Project ◽  
Precipitation And Temperature

Abstract The global climate models (GCMs) of Coupled Model Intercomparison Project phase 6 (CMIP6) were used spatiotemporal projections of precipitation and temperature over Afghanistan for three shared socioeconomic pathways (SSP1-2.6, 2-4.5 and 5-8.5) and two future time horizons, early (2020-2059) and late (2060-2099). The Compromise Programming (CP) approach was employed to order the GCMs based on their skill to replicate precipitation and temperature climatology for the reference period (1975-2014). Three models, namely ACCESS-CM2, MPI-ESM1-2-LR, and FIO-ESM-2-0, showed the highest skill in simulating all three variables, and therefore, were chosen for the future projections. The ensemble mean of the GCMs showed an increase in maximum temperature by 1.5-2.5oC, 2.7-4.3 oC, and 4.5-5.3 oC and minimum temperature by 1.3-1.8 oC, 2.2-3.5 oC, and 4.6-5.2 oC for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively in the later period. Meanwhile, the changes in precipitation in the range of -15-18%, -36-47% and -40-68% for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. The temperature and precipitation were projected to increase in the highlands and decrease over the deserts, indicating dry regions would be drier and wet regions wetter.

scholarly journals The changing nature of hydroclimatic risks across South Africa

2021 ◽  
Vol 168 (3-4) ◽  
Author(s):  
Adam Schlosser ◽  
Andrei Sokolov ◽  
Ken Strzepek ◽  
Tim Thomas ◽  
Xiang Gao ◽  
...  
Keyword(s):  
South Africa ◽  
Air Temperature ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
First Century ◽  
Near Surface ◽  
Climate Targets ◽  
Twenty First Century

AbstractWe present results from large ensembles of projected twenty-first century changes in seasonal precipitation and near-surface air temperature for the nation of South Africa. These ensembles are a result of combining Monte Carlo projections from a human-Earth system model of intermediate complexity with pattern-scaled responses from climate models of the Coupled Model Intercomparison Project Phase 5 (CMIP5). These future ensemble scenarios consider a range of global actions to abate emissions through the twenty-first century. We evaluate distributions of surface-air temperature and precipitation change over three sub-national regions: western, central, and eastern South Africa. In all regions, we find that without any emissions or climate targets in place, there is a greater than 50% likelihood that mid-century temperatures will increase threefold over the current climate’s two-standard deviation range of variability. However, scenarios that consider more aggressive climate targets all but eliminate the risk of these salient temperature increases. A preponderance of risk toward decreased precipitation (3 to 4 times higher than increased) exists for western and central South Africa. Strong climate targets abate evolving regional hydroclimatic risks. Under a target to limit global climate warming to 1.5 °C by 2100, the risk of precipitation changes within South Africa toward the end of this century (2065–2074) is commensurate to the risk during the 2030s without any global climate target. Thus, these regional hydroclimate risks over South Africa could be delayed by 30 years and, in doing so, provide invaluable lead-time for national efforts to prepare, fortify, and/or adapt.

scholarly journals Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on U.S. maize yields

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
David C. Lafferty ◽  
Ryan L. Sriver ◽  
Iman Haqiqi ◽  
Thomas W. Hertel ◽  
Klaus Keller ◽  
...  
Keyword(s):  
Climate Models ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Climate Information ◽  
Model Intercomparison ◽  
Temperature And Precipitation ◽  
Panel Model ◽  
Trade Offs ◽  
Intercomparison Project ◽  
Maize Yields

AbstractEfforts to understand and quantify how a changing climate can impact agriculture often rely on bias-corrected and downscaled climate information, making it important to quantify potential biases of this approach. Here, we use a multi-model ensemble of statistically bias-corrected and downscaled climate models, as well as the corresponding parent models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), to drive a statistical panel model of U.S. maize yields that incorporates season-wide measures of temperature and precipitation. We analyze uncertainty in annual yield hindcasts, finding that the CMIP5 models considerably overestimate historical yield variability while the bias-corrected and downscaled versions underestimate the largest weather-induced yield declines. We also find large differences in projected yields and other decision-relevant metrics throughout this century, leaving stakeholders with modeling choices that require navigating trade-offs in resolution, historical accuracy, and projection confidence.

Application of linear dynamical mode decomposition to surface air temperature in 20th century

2020 ◽  
Author(s):  
Andrey Gavrilov ◽  
Sergey Kravtsov ◽  
Dmitry Mukhin ◽  
Evgeny Loskutov ◽  
Alexander Feigin
Keyword(s):  
20Th Century ◽  
Time Scales ◽  
Air Temperature ◽  
Climate Models ◽  
State Of The Art ◽  
Surface Air Temperature ◽  
Climate Dynamics ◽  
Bayesian Optimization ◽  
Climate Signal ◽  
Function Decomposition

<p>According to recent study [1], the current state-of-the-art climate models lack the substantial part of internal multidecadal climate signal which is observed in the 20th century surface air temperature reanalysis data as a global stadium wave (GSW). In the presented work we further investigate this phenomenon using the recently developed method [2] of empirical spatio-temporal data decomposition into linear dynamical modes (LDMs). The important property of LDMs is their ability to take into account the time scales of the system evolution (they are extracted from observed dataset by the Bayesian optimization technique) better than some other linear techniques, e.g. traditional empirical orthogonal function decomposition. Like any linear decomposition, it provides the time series of principal components and corresponding spatial patterns.<br>We modify the initially developed LDM decomposition to make it possible to take into account a prescribed external forcing (like CO2 emissions, sun activity etc.) and then find part of variability which may be considered as an internal climate dynamics decomposed into set of modes with different time scales, and hence may be helpful in GSW interpretation. The results of applying the method to the 20th century surface air temperature with different ways of forcing inclusion will be presented and discussed.</p><p>1. Kravtsov, S., Grimm, C., & Gu, S. (2018). Global-scale multidecadal variability missing in state-of-the-art climate models. Npj Climate and Atmospheric Science, 1(1), 34. https://doi.org/10.1038/s41612-018-0044-6<br>2. Gavrilov, A., Seleznev, A., Mukhin, D., Loskutov, E., Feigin, A., & Kurths, J. (2018). Linear dynamical modes as new variables for data-driven ENSO forecast. Climate Dynamics. https://doi.org/10.1007/s00382-018-4255-7</p>

scholarly journals BCC-ESM1 Model Datasets for the CMIP6 Aerosol Chemistry Model Intercomparison Project (AerChemMIP)

2021 ◽  
Vol 38 (2) ◽  
pp. 317-328
Author(s):  
Jie Zhang ◽  
Tongwen Wu ◽  
Fang Zhang ◽  
Kalli Furtado ◽  
Xiaoge Xin ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Earth System ◽  
Model Intercomparison ◽  
Aerosol Chemistry ◽  
Temperature And Precipitation ◽  
Intercomparison Project ◽  
The 1960S

AbstractBCC-ESM1 is the first version of the Beijing Climate Center’s Earth System Model, and is participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) is the only CMIP6-endorsed MIP in which BCC-ESM1 is involved. All AerChemMIP experiments in priority 1 and seven experiments in priorities 2 and 3 have been conducted. The DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations have also been run as the entry card of CMIP6. The AerChemMIP outputs from BCC-ESM1 have been widely used in recent atmospheric chemistry studies. To facilitate the use of the BCC-ESM1 datasets, this study describes the experiment settings and summarizes the model outputs in detail. Preliminary evaluations of BCC-ESM1 are also presented, revealing that: the climate sensitivities of BCC-ESM1 are well within the likely ranges suggested by IPCC AR5; the spatial structures of annual mean surface air temperature and precipitation can be reasonably captured, despite some common precipitation biases as in CMIP5 and CMIP6 models; a spurious cooling bias from the 1960s to 1990s is evident in BCC-ESM1, as in most other ESMs; and the mean states of surface sulfate concentrations can also be reasonably reproduced, as well as their temporal evolution at regional scales. These datasets have been archived on the Earth System Grid Federation (ESGF) node for atmospheric chemistry studies.

Evaluating temperature extremes in CMIP6 simulations using statistically proper evaluation methods

2021 ◽  
Author(s):  
Thordis Thorarinsdottir ◽  
Jana Sillmann ◽  
Marion Haugen ◽  
Nadine Gissibl ◽  
Marit Sandstad
Keyword(s):  
Air Temperature ◽  
Model Evaluation ◽  
Climate Models ◽  
Climate Model ◽  
Mean Squared Error ◽  
Surface Air Temperature ◽  
Cmip5 Models ◽  
Model Output ◽  
Intergovernmental Panel ◽  
Near Surface

<p>Reliable projections of extremes in near-surface air temperature (SAT) by climate models become more and more important as global warming is leading to significant increases in the hottest days and decreases in coldest nights around the world with considerable impacts on various sectors, such as agriculture, health and tourism.</p><p>Climate model evaluation has traditionally been performed by comparing summary statistics that are derived from simulated model output and corresponding observed quantities using, for instance, the root mean squared error (RMSE) or mean bias as also used in the model evaluation chapter of the fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). Both RMSE and mean bias compare averages over time and/or space, ignoring the variability, or the uncertainty, in the underlying values. Particularly when interested in the evaluation of climate extremes, climate models should be evaluated by comparing the probability distribution of model output to the corresponding distribution of observed data.</p><p>To address this shortcoming, we use the integrated quadratic distance (IQD) to compare distributions of simulated indices to the corresponding distributions from a data product. The IQD is the proper divergence associated with the proper continuous ranked probability score (CRPS) as it fulfills essential decision-theoretic properties for ranking competing models and testing equality in performance, while also assessing the full distribution.</p><p>The IQD is applied to evaluate CMIP5 and CMIP6 simulations of monthly maximum (TXx) and minimum near-surface air temperature (TNn) over the data-dense regions Europe and North America against both observational and reanalysis datasets. There is not a notable difference between the model generations CMIP5 and CMIP6 when the model simulations are compared against the observational dataset HadEX2. However, the CMIP6 models show a better agreement with the reanalysis ERA5 than CMIP5 models, with a few exceptions. Overall, the climate models show higher skill when compared against ERA5 than when compared against HadEX2. While the model rankings vary with region, season and index, the model evaluation is robust against changes in the grid resolution considered in the analysis.</p>

scholarly journals Seasonal Surface Air Temperature and Precipitation in the FSU Climate Model Coupled to the CLM2

2005 ◽  
Vol 18 (16) ◽  
pp. 3217-3228 ◽  
Author(s):  
D. W. Shin ◽  
S. Cocke ◽  
T. E. LaRow ◽  
James J. O’Brien
Keyword(s):  
Heat Flux ◽  
Air Temperature ◽  
Climate Model ◽  
Initial Conditions ◽  
Sensible Heat Flux ◽  
Surface Air Temperature ◽  
Community Land Model ◽  
Temperature And Precipitation ◽  
The Impact ◽  
Convective Scheme

Abstract The current Florida State University (FSU) climate model is upgraded by coupling the National Center for Atmospheric Research (NCAR) Community Land Model Version 2 (CLM2) as its land component in order to make a better simulation of surface air temperature and precipitation on the seasonal time scale, which is important for crop model application. Climatological and seasonal simulations with the FSU climate model coupled to the CLM2 (hereafter FSUCLM) are compared to those of the control (the FSU model with the original simple land surface treatment). The current version of the FSU model is known to have a cold bias in the temperature field and a wet bias in precipitation. The implementation of FSUCLM has reduced or eliminated this bias due to reduced latent heat flux and increased sensible heat flux. The role of the land model in seasonal simulations is shown to be more important during summertime than wintertime. An additional experiment that assimilates atmospheric forcings produces improved land-model initial conditions, which in turn reduces the biases further. The impact of various deep convective parameterizations is examined as well to further assess model performance. The land scheme plays a more important role than the convective scheme in simulations of surface air temperature. However, each convective scheme shows its own advantage over different geophysical locations in precipitation simulations.

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