scholarly journals Evaluation of Surface Upward Longwave Radiation in the CMIP6 Models with Ground and Satellite Observations

2021 ◽  
Vol 13 (21) ◽  
pp. 4464
Author(s):  
Jiawen Xu ◽  
Xiaotong Zhang ◽  
Chunjie Feng ◽  
Shuyue Yang ◽  
Shikang Guan ◽  
...  
Keyword(s):  
General Circulation ◽  
Bayesian Model Averaging ◽  
Mean Squared Error ◽  
Radiant Energy ◽  
Coupled Model ◽  
Model Averaging ◽  
Longwave Radiation ◽  
Energy System ◽  
General Circulation Models ◽  
The Individual

Surface upward longwave radiation (SULR) is an indicator of thermal conditions over the Earth’s surface. In this study, we validated the simulated SULR from 51 Coupled Model Intercomparison Project (CMIP6) general circulation models (GCMs) through a comparison with ground measurements and satellite-retrieved SULR from the Clouds and the Earth’s Radiant Energy System, Energy Balanced and Filled (CERES EBAF). Moreover, we improved the SULR estimations by a fusion of multiple CMIP6 GCMs using multimodel ensemble (MME) methods. Large variations were found in the monthly mean SULR among the 51 CMIP6 GCMs; the bias and root mean squared error (RMSE) of the individual CMIP6 GCMs at 133 sites ranged from −3 to 24 W m−2 and 22 to 38 W m−2, respectively, which were higher than those found between the CERES EBAF and GCMs. The CMIP6 GCMs did not improve the overestimation of SULR compared to the CMIP5 GCMs. The Bayesian model averaging (BMA) method showed better performance in simulating SULR than the individual GCMs and simple model averaging (SMA) method, with a bias of 0 W m−2 and an RMSE of 19.29 W m−2 for the 133 sites. In terms of the global annual mean SULR, our best estimation for the CMIP6 GCMs using the BMA method was 392 W m−2 during 2000–2014. We found that the SULR varied between 386 and 393 W m−2 from 1850 to 2014, exhibiting an increasing tendency of 0.2 W m−2 per decade (p < 0.05).

scholarly journals Evaluation of Bayesian Multimodel Estimation in Surface Incident Shortwave Radiation Simulation over High Latitude Areas

2019 ◽  
Vol 11 (15) ◽  
pp. 1776 ◽  
Author(s):  
Weiyu Zhang ◽  
Xiaotong Zhang ◽  
Wenhong Li ◽  
Ning Hou ◽  
Yu Wei ◽  
...  
Keyword(s):  
High Latitude ◽  
General Circulation ◽  
Bayesian Model Averaging ◽  
Radiant Energy ◽  
Coupled Model ◽  
Model Averaging ◽  
Energy System ◽  
General Circulation Models ◽  
Shortwave Radiation ◽  
Data Set

Surface incident shortwave radiation (SSR) is crucial for understanding the Earth’s climate change issues. Simulations from general circulation models (GCMs) are one of the most practical ways to produce long-term global SSR products. Although previous studies have comprehensively assessed the performance of the GCMs in simulating SSR globally or regionally, studies assessing the performance of these models over high-latitude areas are sparse. This study evaluated and intercompared the SSR simulations of 48 GCMs participating in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) using quality-controlled SSR surface measurements at 44 radiation sites from three observation networks (GC-NET, BSRN, and GEBA) and the SSR retrievals from the Clouds and the Earth’s Radiant Energy System, Energy Balanced and Filled (CERES EBAF) data set over high-latitude areas from 2000 to 2005. Furthermore, this study evaluated the performance of the SSR estimations of two multimodel ensemble methods, i.e., the simple model averaging (SMA) and the Bayesian model averaging (BMA) methods. The seasonal performance of the SSR estimations of individual GCMs, the SMA method, and the BMA method were also intercompared. The evaluation results indicated that there were large deficiencies in the performance of the individual GCMs in simulating SSR, and these GCM SSR simulations did not show a tendency to overestimate the SSR over high-latitude areas. Moreover, the ensemble SSR estimations generated by the SMA and BMA methods were superior to all individual GCM SSR simulations over high-latitude areas, and the estimations of the BMA method were the best compared to individual GCM simulations and the SMA method-based estimations. Compared to the CERES EBAF SSR retrievals, the uncertainties of the SSR estimations of the GCMs, the SMA method, and the BMA method are relatively large during summer.

scholarly journals Representing the Australian Heat Low in a GCM Using Different Surface and Cloud Schemes

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Matthew M. Allcock ◽  
Duncan Ackerley
Keyword(s):  
General Circulation ◽  
Thermal Inertia ◽  
Coupled Model ◽  
General Circulation Models ◽  
Significant Feature ◽  
Monsoon Circulation ◽  
North West ◽  
Australian Monsoon ◽  
The Individual ◽  
Heat Low

The high insolation during the Southern Hemisphere summer leads to the development of a heat low over north-west Australia, which is a significant feature of the monsoon circulation. It is therefore important that General Circulation Models (GCMs) are able to represent this feature well in order to adequately represent the Australian Monsoon. Given that there are many different configurations of GCMs used globally (such as those used as part of the Coupled Model Intercomparison Project), it is difficult to assess the underlying causes of the differences in circulation between such GCMs. In order to address this problem, the work presented here makes use of three different configurations of the Australian Community Climate and Earth System Simulator (ACCESS). The configurations incorporate changes to the surface parameterization, cloud parameterization, and both together (surface and cloud) while keeping all other parameterized processes unchanged. The work finds that the surface scheme has a larger impact on the heat low than the cloud scheme, which is caused by differences in the soil thermal inertia. This study also finds that the differences in the circulation caused by changing the cloud and surface schemes together are the linear sum of the individual perturbations (i.e., no nonlinear interaction).

Seasonal Forecasts of Australian Rainfall through Calibration and Bridging of Coupled GCM Outputs

2014 ◽  
Vol 142 (5) ◽  
pp. 1758-1770 ◽  
Author(s):  
Andrew Schepen ◽  
Q. J. Wang ◽  
David E. Robertson
Keyword(s):  
General Circulation ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
General Circulation Models ◽  
Seasonal Rainfall ◽  
Climate Indices ◽  
Seasonal Forecasts ◽  
Spatial Coverage ◽  
Validation Procedure ◽  
Systematic Biases

Abstract Coupled general circulation models (GCMs) are increasingly being used to forecast seasonal rainfall, but forecast skill is still low for many regions. GCM forecasts suffer from systematic biases, and forecast probabilities derived from ensemble members are often statistically unreliable. Hence, it is necessary to postprocess GCM forecasts to improve skill and statistical reliability. In this study, the authors compare three methods of statistically postprocessing GCM output—calibration, bridging, and a combination of calibration and bridging—as ways to treat these problems and make use of multiple GCM outputs to increase the skill of Australian seasonal rainfall forecasts. Three calibration models are established using ensemble mean rainfall from three variants of the Predictive Ocean Atmosphere Model for Australia (POAMA) version M2.4 as predictors. Six bridging models are established using POAMA forecasts of seasonal climate indices as predictors. The calibration and bridging forecasts are merged through Bayesian model averaging. Forecast attributes including skill, sharpness, and reliability are assessed through a rigorous leave-three-years-out cross-validation procedure for forecasts of 1-month lead time. While there are overlaps in skill, there are regions and seasons where the calibration or bridging forecasts are uniquely skillful. The calibration forecasts are more skillful for January–March (JFM) to June–August (JJA). The bridging forecasts are more skillful for July–September (JAS) to December–February (DJF). Merging calibration and bridging forecasts retains, and in some seasons expands, the spatial coverage of positive skill achieved by the better of the calibration forecasts and bridging forecasts individually. The statistically postprocessed forecasts show improved reliability compared to the raw forecasts.

Challenging CMIP6 model predictions

2020 ◽  
Author(s):  
Miklos Zagoni
Keyword(s):  
Greenhouse Effect ◽  
General Circulation ◽  
Regional Distribution ◽  
Coupled Model ◽  
Longwave Radiation ◽  
General Circulation Models ◽  
Global Constraints ◽  
Summary Table ◽  
Clear Sky ◽  
Downward Longwave Radiation

&lt;p&gt;The WCRP Coupled Model Intercomparison Project (CMIP) simulations expect increasing downward longwave radiation (DLR, surface LW down) from a human-enhanced greenhouse effect during the 21&lt;sup&gt;st&lt;/sup&gt; century in the range of 10 &amp;#8211; 40 Wm&lt;sup&gt;-2&lt;/sup&gt;. We announce a public challenge to these predictions based on a long known but rarely referred theoretical constraint. Following the logic of original radiative transfer equations of Schwarzschild (1906, Eq. 11), a relationship connects surface net radiation to the effective emission, independent of the optical depth. This relationship is reproduced by several textbooks on atmospheric radiation like Goody (1964, Eq. 2.115), Goody and Yung (1989, Eq. 2.146), Houghton (2002, Eq. 2.13), Pierrehumbert (2010, Eq. 4.44-4.45). In CERES notation: Surface [shortwave (SW) + longwave (LW)] net = OLR/2. A specific &amp;#8220;gross&amp;#8221; version is: Surface (SW net + LW down) = 2OLR. These are for the cloudless case. Their all-sky form includes longwave cloud radiative effect (LWCRE): Surface SW+LW net = (OLR &amp;#8211; LWCRE)/2 and Surface (SW net + LW down) = 2OLR + LWCRE. Controlling these four equations on CERES EBAF Edition 4.1, 18 years of data, and on EBAF Ed4.1 Data Quality Summary Table 2-1 and Table 4-1, each of them is valid within 3 Wm&lt;sup&gt;-2&lt;/sup&gt;. The all-sky versions are satisfied by the IPCC-AR5 (2013) global energy budget (Fig. 2.11) and a water cycle assessment (Stephens and L'Ecuyer 2015) within 2 Wm&lt;sup&gt;-2&lt;/sup&gt;. We couldn't find any reference to these equalities in the literature on general circulation models or climate sensitivity. Applying known definitions, the equations can be solved for LWCRE, resulting in a set of small integers (Zagoni, EGU2019). All-sky fluxes: Surface SW net = &lt;strong&gt;6&lt;/strong&gt;; Surface LW net = &lt;strong&gt;&amp;#8211;2&lt;/strong&gt;; DLR = &lt;strong&gt;13&lt;/strong&gt;; OLR = &lt;strong&gt;9&lt;/strong&gt;. Clear-sky fluxes: Surface SW net = &lt;strong&gt;8&lt;/strong&gt;; Surface LW net = &lt;strong&gt;&amp;#8211;3&lt;/strong&gt;; DLR = &lt;strong&gt;12&lt;/strong&gt;; OLR = &lt;strong&gt;10; &lt;/strong&gt;Surface LW up (ULW) = &lt;strong&gt;15 (&lt;/strong&gt;both for all-sky and clear-sky)&lt;strong&gt;; &lt;/strong&gt;LWCRE (surface and TOA) = &lt;strong&gt;1. &lt;/strong&gt;From this solution it comes for all-sky: DLR = (&lt;strong&gt;13&lt;/strong&gt;/&lt;strong&gt;9&lt;/strong&gt;)OLR, ULW = (&lt;strong&gt;15&lt;/strong&gt;/&lt;strong&gt;9&lt;/strong&gt;)OLR, and for clear-sky ULW = (&lt;strong&gt;15&lt;/strong&gt;/&lt;strong&gt;10&lt;/strong&gt;)OLR. Since the physical principles and conditions behind these equations are solid and justified by observations, we expect them to remain valid in the forthcoming decades as well. CMIP6 models might represent regional distribution changes and cloud feedbacks correctly, in lack of global constraints they may lead to profoundly different outcomes in the long run. This is a testable difference. To check the robustness and stationarity of our equations, we challenge published CMIP5 predictions. We predict for the 21&lt;sup&gt;st&lt;/sup&gt; century: all-sky DLR = (&lt;strong&gt;13&lt;/strong&gt;/&lt;strong&gt;9&lt;/strong&gt;)OLR &amp;#177; 3.0 Wm&lt;sup&gt;-2&lt;/sup&gt;; ULW = (&lt;strong&gt;15&lt;/strong&gt;/&lt;strong&gt;9&lt;/strong&gt;)OLR &amp;#177; 3.0 Wm&lt;sup&gt;-2&lt;/sup&gt; and clear-sky ULW = (&lt;strong&gt;15&lt;/strong&gt;/&lt;strong&gt;10&lt;/strong&gt;)OLR &amp;#177; 3.0 Wm&lt;sup&gt;-2&lt;/sup&gt;. Initial status (CERES EBAF Edition 4.1 annual global means for 2018): all-sky OLR = 240.14, DLR = 344.82, ULW = 399.37, hence all-sky DLR = (&lt;strong&gt;13&lt;/strong&gt;/&lt;strong&gt;9&lt;/strong&gt;)OLR &amp;#8211; 2.05 and ULW = (&lt;strong&gt;15&lt;/strong&gt;/&lt;strong&gt;9&lt;/strong&gt;)OLR &amp;#8211; 0.86 (Wm&lt;sup&gt;-2&lt;/sup&gt;); clear-sky ULW = 399.05, OLR = 265.80, hence ULW = (&lt;strong&gt;15&lt;/strong&gt;/&lt;strong&gt;10&lt;/strong&gt;)OLR + 0.35 Wm&lt;sup&gt;-2&lt;/sup&gt;. Greenhouse effect: g(theory) = G/ULW = (ULW&amp;#8211;OLR)/ULW = (&lt;strong&gt;15 &lt;/strong&gt;&amp;#8211; &lt;strong&gt;9&lt;/strong&gt;)/&lt;strong&gt;15 &lt;/strong&gt;= 0.4, g(observed) = 0.399.&lt;/p&gt;

scholarly journals The relevance of mid-Holocene Arctic warming to the future

2019 ◽  
Vol 15 (4) ◽  
pp. 1375-1394 ◽  
Author(s):  
Masakazu Yoshimori ◽  
Marina Suzuki
Keyword(s):  
Surface Temperature ◽  
General Circulation ◽  
Temperature Response ◽  
Longwave Radiation ◽  
General Circulation Models ◽  
Cross Model ◽  
Arctic Warming ◽  
Temperature Changes ◽  
The Future ◽  
Arctic Climate Change

Abstract. There remain substantial uncertainties in future projections of Arctic climate change. There is a potential to constrain these uncertainties using a combination of paleoclimate simulations and proxy data, but such a constraint must be accompanied by physical understanding on the connection between past and future simulations. Here, we examine the relevance of an Arctic warming mechanism in the mid-Holocene (MH) to the future with emphasis on process understanding. We conducted a surface energy balance analysis on 10 atmosphere and ocean general circulation models under the MH and future Representative Concentration Pathway (RCP) 4.5 scenario forcings. It is found that many of the dominant processes that amplify Arctic warming over the ocean from late autumn to early winter are common between the two periods, despite the difference in the source of the forcing (insolation vs. greenhouse gases). The positive albedo feedback in summer results in an increase in oceanic heat release in the colder season when the atmospheric stratification is strong, and an increased greenhouse effect from clouds helps amplify the warming during the season with small insolation. The seasonal progress was elucidated by the decomposition of the factors associated with sea surface temperature, ice concentration, and ice surface temperature changes. We also quantified the contribution of individual components to the inter-model variance in the surface temperature changes. The downward clear-sky longwave radiation is one of major contributors to the model spread throughout the year. Other controlling terms for the model spread vary with the season, but they are similar between the MH and the future in each season. This result suggests that the MH Arctic change may not be analogous to the future in some seasons when the temperature response differs, but it is still useful to constrain the model spread in the future Arctic projection. The cross-model correlation suggests that the feedbacks in preceding seasons should not be overlooked when determining constraints, particularly summer sea ice cover for the constraint of autumn–winter surface temperature response.

scholarly journals Precipitation in the Amazon and its relationship with moisture transport and tropical Pacific and Atlantic SST from the CMIP5 simulation

2015 ◽  
Vol 12 (1) ◽  
pp. 671-704 ◽  
Author(s):  
G. Martins ◽  
C. von Randow ◽  
G. Sampaio ◽  
A. J. Dolman
Keyword(s):  
Moisture Transport ◽  
General Circulation ◽  
Regional Analysis ◽  
Coupled Model ◽  
General Circulation Models ◽  
Moisture Convergence ◽  
Cmip5 Models ◽  
Wet Season ◽  
The Pacific ◽  
Eastern Edge

Abstract. Studies on numerical modeling in Amazonia show that the models fail to capture important aspects of climate variability in this region and it is important to understand the reasons that cause this drawback. Here, we study how the general circulation models of the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulate the inter-relations between regional precipitation, moisture convergence and Sea Surface Temperature (SST) in the adjacent oceans, to assess how flaws in the representation of these processes can translate into biases in simulated rainfall in Amazonia. Using observational data (GPCP, CMAP, ERSST.v3, ERAI and evapotranspiration) and 21 numerical simulations from CMIP5 during the present climate (1979–2005) in June, July and August (JJA) and December, January and February (DJF), respectively, to represent dry and wet season characteristics, we evaluate how the models simulate precipitation, moisture transport and convergence, and pressure velocity (omega) in different regions of Amazonia. Thus, it is possible to identify areas of Amazonia that are more or less influenced by adjacent ocean SSTs. Our results showed that most of the CMIP5 models have poor skill in adequately representing the observed data. The regional analysis of the variables used showed that the underestimation in the dry season (JJA) was twice in relation to rainy season as quantified by the Standard Error of the Mean (SEM). It was found that Atlantic and Pacific SSTs modulate the northern sector of Amazonia during JJA, while in DJF Pacific SST only influences the eastern sector of the region. The analysis of moisture transport in JJA showed that moisture preferentially enters Amazonia via its eastern edge. In DJF this occurs both via its northern and eastern edge. The moisture balance is always positive, which indicates that Amazonia is a source of moisture to the atmosphere. Additionally, our results showed that during DJF the simulations in northeast sector of Amazonia have a strong bias in precipitation and an underestimation of moisture convergence due to the higher influence of biases in the Pacific SST. During JJA, a strong precipitation bias was observed in the southwest sector associated, also with a negative bias of moisture convergence, but with weaker influence of SSTs of adjacent oceans. The poor representation of precipitation-producing systems in Amazonia by the models and the difficulty of adequately representing the variability of SSTs in the Pacific and Atlantic oceans may be responsible for these underestimates in Amazonia.

scholarly journals PLASIM-ENTSem: a spatio-temporal emulator of future climate change for impacts assessment

2013 ◽  
Vol 6 (2) ◽  
pp. 3349-3380 ◽  
Author(s):  
P. B. Holden ◽  
N. R. Edwards ◽  
P. H. Garthwaite ◽  
K. Fraedrich ◽  
F. Lunkeit ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
General Circulation ◽  
Coupled Model ◽  
Co2 Concentration ◽  
General Circulation Models ◽  
Climate Impacts ◽  
Future Climate ◽  
Future Climate Change ◽  
Climate Data ◽  
Spatio Temporal

Abstract. Many applications in the evaluation of climate impacts and environmental policy require detailed spatio-temporal projections of future climate. To capture feedbacks from impacted natural or socio-economic systems requires interactive two-way coupling but this is generally computationally infeasible with even moderately complex general circulation models (GCMs). Dimension reduction using emulation is one solution to this problem, demonstrated here with the GCM PLASIM-ENTS. Our approach generates temporally evolving spatial patterns of climate variables, considering multiple modes of variability in order to capture non-linear feedbacks. The emulator provides a 188-member ensemble of decadally and spatially resolved (~ 5° resolution) seasonal climate data in response to an arbitrary future CO2 concentration and radiative forcing scenario. We present the PLASIM-ENTS coupled model, the construction of its emulator from an ensemble of transient future simulations, an application of the emulator methodology to produce heating and cooling degree-day projections, and the validation of the results against empirical data and higher-complexity models. We also demonstrate the application to estimates of sea-level rise and associated uncertainty.

scholarly journals Skillful Climate Forecasts of the Tropical Indo-Pacific Ocean Using Model-Analogs

2018 ◽  
Vol 31 (14) ◽  
pp. 5437-5459 ◽  
Author(s):  
Hui Ding ◽  
Matthew Newman ◽  
Michael A. Alexander ◽  
Andrew T. Wittenberg
Keyword(s):  
General Circulation ◽  
General Circulation Models ◽  
Coarse Grained ◽  
Seasonal Forecasts ◽  
Multimodel Ensemble ◽  
Initial State ◽  
The North ◽  
Eastern Equatorial Pacific ◽  
Spread Model ◽  
The Individual

Seasonal forecasts made by coupled atmosphere–ocean general circulation models (CGCMs) undergo strong climate drift and initialization shock, driving the model state away from its long-term attractor. Here we explore initializing directly on a model’s own attractor, using an analog approach in which model states close to the observed initial state are drawn from a “library” obtained from prior uninitialized CGCM simulations. The subsequent evolution of those “model-analogs” yields a forecast ensemble, without additional model integration. This technique is applied to four of the eight CGCMs comprising the North American Multimodel Ensemble (NMME) by selecting from prior long control runs those model states whose monthly tropical Indo-Pacific SST and SSH anomalies best resemble the observations at initialization time. Hindcasts are then made for leads of 1–12 months during 1982–2015. Deterministic and probabilistic skill measures of these model-analog hindcast ensembles are comparable to those of the initialized NMME hindcast ensembles, for both the individual models and the multimodel ensemble. In the eastern equatorial Pacific, model-analog hindcast skill exceeds that of the NMME. Despite initializing with a relatively large ensemble spread, model-analogs also reproduce each CGCM’s perfect-model skill, consistent with a coarse-grained view of tropical Indo-Pacific predictability. This study suggests that with little additional effort, sufficiently realistic and long CGCM simulations provide the basis for skillful seasonal forecasts of tropical Indo-Pacific SST anomalies, even without sophisticated data assimilation or additional ensemble forecast integrations. The model-analog method could provide a baseline for forecast skill when developing future models and forecast systems.

Evaluation of the tail of the probability distribution of daily and sub-daily precipitation in CMIP6 models

2021 ◽  
pp. 1-61
Author(s):  
Jesse Norris ◽  
Alex Hall ◽  
J. David Neelin ◽  
Chad W. Thackeray ◽  
Di Chen
Keyword(s):  
General Circulation ◽  
Daily Precipitation ◽  
Precipitation Amount ◽  
Coupled Model ◽  
Lower Troposphere ◽  
General Circulation Models ◽  
Intercomparison Project ◽  
Cloud Tops ◽  
The Tropics ◽  
Fraction Models

AbstractDaily and sub-daily precipitation extremes in historical Coupled-Model-Intercomparison-Project-Phase-6 (CMIP6) simulations are evaluated against satellite-based observational estimates. Extremes are defined as the precipitation amount exceeded every x years, ranging from 0.01–10, encompassing the rarest events that are detectable in the observational record without noisy results. With increasing temporal resolution there is an increased discrepancy between models and observations: for daily extremes the multi-model median underestimates the highest percentiles by about a third, and for 3-hourly extremes by about 75% in the tropics. The novelty of the current study is that, to understand the model spread, we evaluate the 3-D structure of the atmosphere when extremes occur. In midlatitudes, where extremes are simulated predominantly explicitly, the intuitive relationship exists whereby higher-resolution models produce larger extremes (r=–0.49), via greater vertical velocity. In the tropics, the convective fraction (the fraction of precipitation simulated directly from the convective scheme) is more relevant. For models below 60% convective fraction, precipitation amount decreases with convective fraction (r=–0.63), but above 75% convective fraction, this relationship breaks down. In the lower-convective-fraction models, there is more moisture in the lower troposphere, closer to saturation. In the higher-convective-fraction models, there is deeper convection and higher cloud tops, which appears to be more physical. Thus, the low-convective models are mostly closer to the observations of extreme precipitation in the tropics, but likely for the wrong reasons. These inter-model differences in the environment in which extremes are simulated hold clues into how parameterizations could be modified in general circulation models to produce more credible 21st-Century projections.

scholarly journals Performance Assessment of General Circulation Model in Simulating Daily Precipitation and Temperature Using Multiple Gridded Datasets

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1793 ◽  
Author(s):  
Najeebullah Khan ◽  
Shamsuddin Shahid ◽  
Kamal Ahmed ◽  
Tarmizi Ismail ◽  
Nadeem Nawaz ◽  
...  
Keyword(s):  
General Circulation ◽  
Coupled Model ◽  
Circulation Model ◽  
General Circulation Models ◽  
Daily Maximum ◽  
Gridded Data ◽  
Max Planck ◽  
Meteorological Forcing ◽  
Temperature And Precipitation ◽  
Feature Selection Approach

The performance of general circulation models (GCMs) in a region are generally assessed according to their capability to simulate historical temperature and precipitation of the region. The performance of 31 GCMs of the Coupled Model Intercomparison Project Phase 5 (CMIP5) is evaluated in this study to identify a suitable ensemble for daily maximum, minimum temperature and precipitation for Pakistan using multiple sets of gridded data, namely: Asian Precipitation–Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE), Berkeley Earth Surface Temperature (BEST), Princeton Global Meteorological Forcing (PGF) and Climate Prediction Centre (CPC) data. An entropy-based robust feature selection approach known as symmetrical uncertainty (SU) is used for the ranking of GCM. It is known from the results of this study that the spatial distribution of best-ranked GCMs varies for different sets of gridded data. The performance of GCMs is also found to vary for both temperatures and precipitation. The Commonwealth Scientific and Industrial Research Organization, Australia (CSIRO)-Mk3-6-0 and Max Planck Institute (MPI)-ESM-LR perform well for temperature while EC-Earth and MIROC5 perform well for precipitation. A trade-off is formulated to select the common GCMs for different climatic variables and gridded data sets, which identify six GCMs, namely: ACCESS1-3, CESM1-BGC, CMCC-CM, HadGEM2-CC, HadGEM2-ES and MIROC5 for the reliable projection of temperature and precipitation of Pakistan.

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