Performance Of Global Climate Models Research Articles

Accuracy of historical precipitation from CMIP6 global climate models under diversified climatic features over India

The importance of global climate models (GCMs) is increasingly recognized due to their excellent ability to accurately predict climatic factors. These capabilities prove invaluable to water resources engineers as they facilitate effective planning and strategic decision-making. Finally, evaluating the performance of GCMs is very important because it allows us to simulate and predict different climate scenarios, empowering us to make informed choices. Therefore, the purpose of this study is to determine the degree of discordance between historical simulated data produced by the CMIP6 models and historical observational data over different climate zones of India. The ability of 24 different GCMs to reproduce the geographical and seasonal distribution of Indian precipitation has been tested by analyzing the daily historical precipitation forecasts from these models. These models have been used to estimate the degree of uncertainty associated with the spatiotemporal variability of precipitation forecasts. More than 20% percent bias (PBIAS) is observed to occur predominantly in four climate classifications: polar tundra, temperate, cold, and tropical monsoon. In some regions of India, the CMIP6 models produce overestimated or underestimated results. The locations identified indicate that there have been changes of more than 20% PBIAS near Sivalik Range, Naga Hills, and Western Ghats. The precipitations of those regions that have been underestimated also imply that those locations have different climatic conditions. This study also highlights that CMIP6 GCMs are yet to produce better results near several Indian mountainous regions depending upon climates. The outcomes of this study will be very useful for reconstructing modeled data for that specific regions.

Enhancing reliability in climate projections: A novel approach for selecting global climate models

Reliable climate projections are essential for informed decision-making in the context of climate change. However, selecting suitable Global Climate Models (GCMs) for such projections remains challenging, especially when computational resources are limited. This study introduced an innovative approach to GCM selection, emphasizing the identification of models that exhibit consistency in projecting future climate changes and skill in representing current climate conditions, including average climate, seasonal patterns, and climatic variations. GCM performance in simulating these critical properties was evaluated for three major climatic variables: rainfall, maximum temperature, and minimum temperature. This assessment results in a structured 3 × 3 performance matrix for each GCM, encapsulating its ability to capture these essential climate features. The matrix distances, quantifying the disparities between each GCM's performance matrix and the ideal reference matrix, were used to collectively represent the overall model performance. Finally, GCMs were ranked based on these differences using the Jenks natural break classification method, a robust statistical technique, to aid in identifying the top-performing GCMs, making them ideal candidates for ensemble model construction. The newly developed method was tested by applying it to select GCMs for Nigeria from a pool of 19 CMIP6 GCMs. The results indicate that 15 of 19 GCMs consistently projected future climate within a 95% confidence interval. Further evaluation of matrix distances and natural classification reveals a subset of GCMs, ACCESS.ESM1.5, BCC.CSM2.MR, CMCC.ESM2 and MRI.ESM2.0 are the most suitable choice for simulating Nigeria's climate. The multimodel ensemble mean of the selected GCMs projected a notable increase in rainfall by 10–40% over most of the country and the maximum and minimum temperatures by 1.0–3.5 °C and 0.5–4.0 °C across the country. The method introduced in this study can be an effective tool for GCM selection to enhance climate projection reliability.

Characterization of the future northeast monsoon rainfall based on the clustered climate zone under CMIP6 in Peninsular Malaysia

The Northeast monsoon (NEM) exerts a significant influence on the climate system of Peninsular Malaysia (PM) and has profound implications for water resource management, flood control, and disaster preparedness in the region. This study employed CHIRPS rainfall data from 1981 to 2022 at a spatial resolution of 0.25° to generate climate zones. The performance of global climate models (GCMs) in replicating monthly rainfall was evaluated using multiple statistical metrics, with the top-ranked ones being used for the generation of future projection downscaled ensemble models. The NEM months (November, December, January, February, and March) were used as input parameters to form clustered homogeneous groups using the Ward hierarchical method. The optimal number of climate zones was determined using the elbow method, silhouette width values, and spatial evaluation. Ultimately, a 7-cluster solution was chosen due to its consistency and meaningful cluster formation. The zones were categorized into two regions: the West Coast and the East Coast, based on their geographical location and distinctive monthly NEM rainfall patterns. The analysis of the probability distribution function (PDF) showed that rainfall in the West Coast region had a more spread-out pattern with varying amounts, while rainfall in the East Coast region was more concentrated and consistent. The Northeast climate zone received the highest rainfall, attributed to its proximity to the South China Sea, mountainous areas, and the NEM convergence zone. In contrast, the Northwest and Lower Central-West climate zones received the lowest rainfall due to the rain shadow effect, limited moisture availability, and distance from major moisture sources. The CMCC-ESM2, CMCC-CM2-SR5, EC-Earth3, and CanESM5 consistently ranked among the highest performing models. After linear scaling bias correction, validated GCMs showed significant improvements, with improvements in MAE, NRMSE, PBIAS, RSR, NSE, MD, CP, R2, and KGE by 11–45%, 15–47%, 96–100%, 15–47%, 10–79%, 5–37%, 68–113%, 11–73%, and 9–51%, respectively. Analysis of NEM season rainfall patterns across climate zones indicated consistent mid-future increases for the Northwest and Lower Central-West zones (12.4–19.8%) and the Upper Central-West and Southwest zones (0.2–5.4%) across all scenarios. In the far-future, the Southeast zone saw an overall increase in rainfall (4.9–11.4%), while the Central-West zone exhibited mixed results (−4.0 to 5.6%). The results also highlighted spatial variability and the need to consider specific climate zones when assessing the impacts of climate change on the NEM, emphasizing the importance of incorporating these projections into long-term planning and decision-making processes.

Evaluation of Extreme Climate Indices over the Three Northeastern Provinces of China Based on CMIP6 Models Outputs

This study evaluates the performance of Global Climate Models (GCMs) in simulating extreme climate in three northeastern provinces of China (TNPC). A total of 23 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were selected and compared with observations from 1961 to 2010, using the 12 extreme climate indices defined by the Expert Team on Climate Change Detection and Indicators. The Interannual Variability Skill Score (IVS), Taylor diagrams and Taylor Skill Scores (S) were used as evaluation tools to compare the outputs of these 23 GCMs with the observations. The results show that the monthly minimum of daily minimum temperature (TNn) is overestimated in 55.7% of the regional grids, while the percentage of time when the daily minimum temperature is below the 10th percentile (TN10p) and the monthly mean difference between the daily maximum and minimum temperatures (DTR) are underestimated in more than 95% of the regional grids. The monthly maximum value of the daily maximum temperature (TXx) and the annual count when there are at least six consecutive days of the minimum temperature below the 10th percentile (CSDI) have relatively low regional spatial biases of 1.17 °C and 1.91 d, respectively. However, the regional spatial bias of annual count when the daily minimum temperature is below 0 °C (FD) is relatively high at 9 d. The GCMs can efficiently capture temporal variations in CSDI and TN10p (IVS < 0.5), as well as the spatial patterns of TNn and FD (S > 0.8). For the extreme precipitation indices, GCMs overestimate the annual total precipitation from days greater than the 95th percentile (R95p) and the annual count when precipitation is greater than or equal to 10 mm (R10 mm) in more than 90% of the regional grids. The maximum number of consecutive days when precipitation is below 1 mm (CDD) and the ratio of annual total precipitation to the number of wet days (greater than or equal to 1 mm) (SDII) are underestimated in more than 80% and 54% of the regional grids, respectively. The regional spatial bias of the monthly maximum consecutive 5-day precipitation (RX5day) is relatively small at 10.66%. GCMs are able to better capture temporal variations in the monthly maximum 1-day precipitation (RX1day) and SDII (IVS < 0.6), as well as spatial patterns in R95p and R10mm (S > 0.7). The findings of this study can provide a reference that can inform climate hazard risk management and mitigation strategies for the TNPC.

Evaluation and Projection of Climate Change in the Second Songhua River Basin Using CMIP6 Model Simulations

The aim of this study is to evaluate the performance of the Global Climate Model (GCM) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in historical simulations of temperature and precipitation. The goal is to select the best performing GCMs for future projection of temperature and precipitation in the Second Songhua River Basin under multiple shared socioeconomic pathways (SSPs). Interannual variability skill (IVS) and Taylor diagrams are used to evaluate the spatiotemporal performance of GCMs against temperature and precipitation data published by the China Meteorological Science Commons during 1956–2016. In addition, five relatively independent models are selected to simulate the temperature and precipitation for 2021–2050 using Hierarchical Clustering. The selected models are CMCC-ESM2, EC-Earth3-Veg-LR, IPSL-CM6A-LR, MIROC-ES2L, and MPI-ESM1-2-HR. The projected results find that SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios show an increasing trend of future annual mean temperature and precipitation. However, for annual precipitation, there is a mixed state of increase and decrease among different models on the seasonal scale. In general, future temperature and precipitation changes still show a trend of growth and uneven distribution in the Second Songhua River Basin, which may be further accelerated by human activities.

Microphysical and thermodynamic phase analyses of Arctic low-level clouds measured above the sea ice and the open ocean in spring and summer

Abstract. Airborne in situ cloud measurements were carried out over the northern Fram Strait between Greenland and Svalbard in spring 2019 and summer 2020. In total, 811 min of low-level cloud observations were performed during 20 research flights above the sea ice and the open Arctic ocean with the Polar 5 research aircraft of the Alfred Wegener Institute. Here, we combine the comprehensive in situ cloud data to investigate the distributions of particle number concentration N, effective diameter Deff, and cloud water content CWC (liquid and ice) of Arctic clouds below 500 m altitude, measured at latitudes between 76 and 83∘ N. We developed a method to quantitatively derive the occurrence probability of their thermodynamic phase from the combination of microphysical cloud probe and Polar Nephelometer data. Finally, we assess changes in cloud microphysics and cloud phase related to ambient meteorological conditions in spring and summer and address effects of the sea ice and open-ocean surface conditions. We find median N from 0.2 to 51.7 cm−3 and about 2 orders of magnitude higher N for mainly liquid clouds in summer compared to ice and mixed-phase clouds measured in spring. A southerly flow from the sea ice in cold air outbreaks dominates cloud formation processes at temperatures mostly below −10 ∘C in spring, while northerly warm air intrusions favor the formation of liquid clouds at warmer temperatures in summer. Our results show slightly higher N in clouds over the sea ice compared to the open ocean, indicating enhanced cloud formation processes over the sea ice. The median CWC is higher in summer (0.16 g m−3) than in spring (0.06 g m−3), as this is dominated by the available atmospheric water content and the temperatures at cloud formation level. We find large differences in the particle sizes in spring and summer and an impact of the surface conditions, which modifies the heat and moisture fluxes in the boundary layer. By combining microphysical cloud data with thermodynamic phase information from the Polar Nephelometer, we find mixed-phase clouds to be the dominant thermodynamic cloud phase in spring, with a frequency of occurrence of 61 % over the sea ice and 66 % over the ocean. Pure ice clouds exist almost exclusively over the open ocean in spring, and in summer the cloud particles are most likely in the liquid water state. The comprehensive low-level cloud data set will help us to better understand the role of clouds and their thermodynamic phase in the Arctic radiation budget and to assess the performance of global climate models in a region of the world with the strongest anthropogenic climate change.

Evaluation of global climate models for the simulation of precipitation and maximum and minimum temperatures at coarser and finer resolutions based on temporal and spatial assessment metrics in mainland of China

Abstract Many studies have evaluated the performance of multiple global climate models (GCMs) from a temporal or spatial perspective at finer resolution, but no study has evaluated the performance of individual GCMs at different resolutions and before and after bias correction from both temporal and spatial perspectives. The goal of this study is to evaluate the performance of 21 Coupled Model Inter-comparison Project 6 (CMIP6) GCMs at the raw (coarser) and downscaled (finer) resolutions and after bias correction in relation to their skills in the simulation of daily precipitation and maximum and minimum temperatures over China for the period 1961–2014 using state-of-the-art temporal and spatial metrics, Kolmogorov–Smirnov statistic and SPAtial EFficiency. The results indicated some differences in the ranks of GCMs between temporal and spatial metrics at different resolutions. The overall ranking shows that the simulations at the raw resolution of GCMs are more similar to the observations than the simulations after inverse distance weighted interpolation in SPAtial EFficiency. Three variables from bias-corrected GCMs ranked from 1 to 21 show similar good performance in spatial patterns but the poorest trend in empirical Cumulative Distribution Functions (ECDFs) except daily precipitation.

Importance of Minor‐Looking Treatments in Global Climate Models

AbstractIt is very well known that parameter tuning can have a major impact on the performance of Global Climate Models. However, parameter tuning is not the only implementation detail that can drastically affect model performance and the representation of various phenomena in models. “Minor‐looking treatments” often exert a critical control on model performance; they include lower and upper limits of physical variables, thresholds of variables that control the enabling or disabling of a specific process, whether two schemes can work together or only one scheme works exclusively, and numerical methods including the order of calling various physics schemes. The impacts of such minor‐looking treatments are sometimes comparable to or even larger than those obtained by introducing advanced parameterizations based on theory and observation. Because the importance of these treatments is often overlooked and not discussed in the literature, we comprehensively summarize examples of various minor‐looking treatments that can considerably affect model performance.

Evaluation and projection of precipitation in Pakistan using the Coupled Model Intercomparison Project Phase 6 model simulations

AbstractThis study aimed to evaluate the performance of global climate models (GCMs) from the family of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in the historical simulation of precipitation and select the best performing GCMs for future projection of precipitation in Pakistan under multiple shared socioeconomic pathways (SSPs). The spatiotemporal performance of GCMs was evaluated against the Climate Research Unit (CRU) data in simulating annual precipitation during 1951–2014, using the Taylor diagram and interannual variability skill (IVS). Moreover, the modified Mann–Kendall (mMK) and Sen's slope estimator (SSE) tests were employed to estimate significant trends in future precipitation for the period 2015–2100. Based on the comprehensive ranking index (CRI), the HadGEM3‐GC31‐MM model has the highest skill in simulating precipitation distributions followed by EC‐Earth3‐Veg‐LR, CNRM‐ESM2‐1, MPI‐ESM1‐2‐HR, CNRM‐CM6‐1, MRI‐ESM2‐0, CNRM‐CM6‐1‐HR, EC‐Earth3‐Veg, MCM‐UA‐1‐0, INM‐CM5‐0, KACE‐1‐0‐G, CAMS‐CSM1‐0, and HadGEM3‐GC31‐LL models. Furthermore, the projections of the best models ensemble mean (BMEM) showed that the study region will experience a substantial increase in precipitation under SSP3‐7.0 and SSP5‐8.5 but an indolent rise under SSP1‐2.6 and SSP2‐4.5 scenarios. The summer and annual precipitations exhibit a statistically significant increasing trend relative to the winter season under most scenarios. Moreover, the magnitude of monotonic trends in seasonal and annual precipitation progresses from low forcing scenario (SSP1‐2.6) to high forcing scenario (SSP5‐8.5). The findings of the study could provide a benchmark in selecting appropriate GCMs for future projection over a data scare region, like Pakistan. Moreover, the projected trends of future precipitation are crucial in devising adaption and mitigation actions towards sustainable planning of water resource management, food security, and disaster risk management.

Simulation of Indian summer monsoon rainfall, interannual variability and teleconnections: evaluation of CMIP6 models

We analyse the performance of global climate models of 6 $$\mathrm{th}$$ generation of Coupled Model Intercomparison Project (CMIP6) in simulating climatological summer monsoon rainfall over India, interannual variability (IAV) of all-India summer monsoon rainfall (ISMR) and its teleconnections with rainfall variability over equatorial Pacific and Indian Oceans. The multimodel ensemble mean (MME) of 61 CMIP6 models shows the best skill in simulating mean monsoon rainfall over India compared to the MMEs of 6 $$\mathrm{th}$$ generation atmosphere-only models (AMIP6) and the previous generations of Atmospheric and Coupled Model Intercomparison Projects (AMIPs and CMIPs). Systematic improvement and reduction in bias are evident from lower to higher AMIPs/CMIPs. Still, there exists dry bias over a narrow region of the monsoon zone of central India besides wet and cold bias over the surrounding oceans. The persistence of errors in atmosphere-only models hints that the source of errors could be with atmosphere models. Fifteen CMIP6 models selected through objective criteria, perform the best in simulating mean monsoon, IAV of ISMR, the strong inverse relationship between ISMR and Boreal summer El Niño-Southern Oscillation (ENSO), and the inverse relationship between all-India rainfall and north–west tropical Pacific rainfall in June. Several models reproduce the dipole structure of Equatorial Indian Ocean Oscillation (EQUINOO) with the centres over western and eastern equatorial Indian Ocean. But, ISMR-EQUINOO relationship in many of them is opposite to the observed. Our analysis implies the need for capturing ISMR-EQUINOO link to improve the simulation of IAV of ISMR which is crucial for reliable monsoon prediction and projection.

Observed and projected trends in climate extremes in a tropical highland region: An agroecosystem perspective

AbstractTropical highland environments present substantial challenges for climate projections due to sparse observations, significant local heterogeneity and inconsistent performance of global climate models (GCMs). Moreover, these areas are often densely populated, with agriculture‐based livelihoods sensitive to transient climate extremes not always included in available climate projections. In this context, we present an analysis of observed and projected trends in temperature and precipitation extremes across agroecosystems (AESs) in the northwest Ethiopian Highlands, to provide more relevant information for adaptation. Limited observational networks are supplemented with a satellite‐station hybrid product, and trends are calculated locally and summarized at the adaptation‐relevant unit of the AES. Projections are then presented from GCM realizations with divergent climate projections, and results are interpreted in the context of agricultural climate sensitivities. Trends in temperature extremes (1981–2016) are typically consistent across sites and AES, but with different implications for agricultural activities in the other AES. Trends in temperature extremes from GCM projected data also generally have the same sign as the observed trends. For precipitation extremes, there is greater site‐to‐site variability. Summarized by AES, however, there is a clear tendency towards reduced precipitation, associated with decreases in wet extremes and a tendency towards temporally clustered wet and dry days. Over the retrospective analysis period, neither of the two analysed GCMs captures these trends. Future projections from both GCMs include significant wetting and an increase in precipitation extremes across AES. However, given the lack of agreement between GCMs and observations with respect to trends in recent decades, the reliability of these projections is questionable. The present study is consistent with the “East Africa Paradox” that observations show drying in summer season rainfall while GCMs project wetting. This has an expression in summertime Ethiopian rain that has not received significant attention in previous studies.

Mechanism of the Centennial Subpolar North Atlantic Cooling Trend in the FGOALS‐g2 Historical Simulation

AbstractA cold blob, manifested as a centennial cooling trend in sea surface temperature (SST), is observed in the mid‐latitude North Atlantic. The presence of the cold blob is hypothesized as an evidence of a slowdown of Atlantic Meridional Overturning Circulation (AMOC), based on paleoclimate proxies and global climate models (GCMs). However, the performance of GCMs in simulating the cold blob remains unsatisfactory in terms of the SST cooling rate and the spatial extent. This study investigates the forcing mechanism of the cold blob using the Flexible Global Ocean‐Atmosphere‐Land System Model Grid‐point version 2 (FGOALS‐g2), a GCM that reasonably simulates the observed cooling trend in the subpolar North Atlantic and its spatial pattern. Surface heat budget analysis suggests that the cold blob is largely a result of the imbalance between changes in the heat storage and surface turbulent heat fluxes, exhibiting a cooling and warming effect, respectively. Investigation of ocean heat content indicates that heat advection into the cold blob region has decreased, mainly due to ocean circulation changes. However, in the FGOALS‐g2, both the AMOC slowdown and the reduced meridional heat transport explain a limited portion (less than 50%) of the cold blob sea surface temperature anomaly trend. Overall, complementing existing studies that attribute the cold blob to an AMOC slowdown, our results suggest that additional processes, including subpolar gyre circulation and a synergy between the atmosphere and the ocean, are at work in the formation of the cold blob.

Comparison of CMIP6 and CMIP5 model performance in simulating historical precipitation and temperature in Bangladesh: a preliminary study

The relative performance of global climate models (GCMs) of phases 5 and 6 of the coupled model intercomparison project (CMIP5 and CMIP6, respectively) was assessed in this study based on their ability to simulate annual and seasonal mean rainfall and temperature over Bangladesh for the period 1977–2005. Multiple statistical metrics were used to measure the performance of the GCMs at 30 meteorological observation stations. Two robust multi-criteria decision analysis methods were used to integrate the results obtained using different metrics for an unbiased ranking of the GCMs. The results revealed MIROC5 as the most skillful among CMIP5 GCMs and ACCESS-CM2 among CMIP6 GCMs. Overall, CMIP6 MME showed a significant improvement in simulating rainfall and temperature over Bangladesh compared to CMIP5 MME. The highest improvements were found in simulating cold season (winter and post-monsoon) rainfall and temperature in higher elevated areas. The improvement was relatively more for rainfall than for temperature. The models could capture the interannual variability of annual and seasonal rainfall and temperature reliably, except for the winter rainfall. However, systematic wet and cold/warm biases still exist in CMIP6 models for Bangladesh. CMIP6 GCMs showed higher spatial correlations with observed data, but the higher difference in standard deviations and centered root mean square errors compared to CMIP5 GCMs indicates better performance in simulating geographical distribution but lower performance in simulating spatial variability of most of the climate variables except for minimum temperature at different timescales. In terms of Taylor skill score, the CMIP6 MME showed higher performance in simulating rainfall but lower performance in simulating temperature than CMIP5 MME for most of the timeframes. The findings of this study suggest that the added value of rainfall and temperature simulations in CMIP6 models is not consistent among the climate models used in this research. However, it sets a precedent for future research on climate change risk assessment for the scientific community.

Assessment of the uncertainties of global climate models in the evaluation of standardized precipitation and runoff indices: a case study

ABSTRACT Uncertainties in climate change projection can originate from various sources and cause challenges. Thus, two specific approaches were developed in this study, for use in the selection of global climate models and in the assessment of drought occurrence. Considering the bias-corrected data, the performance of global climate models was evaluated using statistical methods, and the 14 best-ranked models were selected. These climate scenarios were used in the Long Ashton Research Station (LARS) downscaling model to obtain the precipitation and temperature time series. Identification of unit Hydrographs And Component flows from Rainfall, Evaporation, and Streamflow (IHACRES) was used to model the runoff time series. Standardized precipitation and runoff indices were considered to assess the probability of meteorological and hydrological droughts. Finally, the Bayesian method was used to analyse the uncertainty assessment of drought occurrence. This methodology was applied in the Karkheh River basin and presented the moderate drought condition as the most probable state.

Multi-model ensemble mean of global climate models fails to reproduce early twentieth century Arctic warming

The Arctic climate system is very sensitive to external perturbations, which results in more rapid surface air temperature (SAT) changes in the Arctic compared to lower latitudes. This study aims at assessing the performance of global climate models from the phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6) in reproducing the periods of Arctic warming and cooling known from the observations. Two experiments are considered for 9 CMIP5 models, and one experiment is used for the 108 ensemble members from 32 CMIP6 models. The ensemble mean of unforced Arctic amplification variability in the CMIP5 pre-industrial control experiment suggests a crucial role of external forcings in the present Arctic amplification. The ensemble mean of the same models in the historical experiment shows a century-long upward trend and clearly reproduces the present-day Arctic amplification. However, there is no consistent response of increasing Arctic amplification in the early twentieth century warming period during the 1920s–1940s in the CMIP5 models. This indicates the 1920s–1940s Arctic warming was unlikely to happen due to external forcings. Using the large CMIP6 ensemble in the historical experiment we show that the multi-model ensemble mean in the new generation of high-resolution CMIP6 models does not reproduce that warming either, thus posing questions how well the models capture internal climate variability and distinguish it from natural and anthropogenic forcings. This issue ultimately affects the reliability of future Arctic climate projections.

Climate and hydrologic ensembling lead to differing streamflow and sediment yield predictions

Climate change is leading to alterations of the hydrologic cycle and sediment movement within watersheds, but the details and impacts of these changes are indeterminate. To reduce this uncertainty, many researchers create ensembles by averaging the projected temperature and precipitation from multiple global climate model (GCM) ensemble members before running these as forcing inputs through hydrologic models. There is little research quantifying if these ensembled climate scenarios produce similar hydrologic model results to those based on individual ensemble members. We created multiple sets of ensembled climate inputs for a pair of hydrologic and sediment yield models of adjacent watersheds that drain to the Great Lakes. We then compared the hydrologic and sediment results of the models forced by these ensembled climate scenarios with hydrologic ensembles created by running the individual climate ensemble members through the same hydrologic models. We found that, in all cases, the streamflow and sediment yield results are significantly different at the 5% confidence level and the ensembled climate scenarios can lead to systematic negative biases. We also looked at three subset hydrologic ensembles: all 10 CMIP5 ensemble members from the CSIRO mk3.6 model; a Representative ensemble with high, moderate, and low precipitation predictions; and a Best Fit ensemble based on GCM performance relative to historic climate. We found that the subset ensembles covered a large portion of the range of outputs for the whole set, while producing mean annual streamflows within 5.5% of the full hydrologic ensemble results and sediment yield and sediment discharge results within 12.2%.

On the uncertainty of future projections of Marine Heatwave events in the North Atlantic Ocean

Marine Heatwave (MHW) events have been increasing all around the world, causing severe impacts on marine ecosystems and on the economy of the aquaculture, fishing and tourism industries. In this study, the occurrence and characteristics of MHW events in the North Atlantic are analyzed for a recent period (1971–2000) and the two future periods (2041–2070 and 2071–2100). The analysis is based on Sea Surface Temperature (SST) data obtained from observations and from Global Climate Models (GCM) in the CMIP5 archieve. The results show that the GCMs present significant shortcomings in reproducing the SST and MHWs. The model results regarding the recent trends in MHW are in agreement with the ones observed, although they underestimate the values of MHW intensity and overestimate the values of both frequency and duration of events. The MHWs observed occur at a mean frequency of 1.90 events per year and are characterized by ~ 13 days of mean duration and 0.37 oC of mean intensity, while the multi-model ensemble mean characterizes the events with 12 additional days and 0.15 oC less intensity. Under climate change scenarios, when considering a stationary threshold, the models project noticeable increases in MHW event intensity that could reach 2 oC above the 90th percentile and a quasi-permanent state of MHW by the end of the century. When a non-stationary threshold is considered, the characteristics of the events are similar to those obtained during the historical period. The GCMs significant biases in simulating the SST and leading to extremes like MHWs highlight the importance of improving GCM performance.

Skill assessment of global climate model wind speed from CMIP5 and CMIP6 and evaluation of projections for the Bay of Bengal

Atmospheric and oceanic parameters derived from global climate model (GCM) simulations have received wide global attention and importance in representing the future world under different scenarios of greenhouse gas emissions. The present study deals with near-surface wind speed in the Bay of Bengal (BoB) obtained from CMIP5 and the upcoming CMIP6 GCMs and validation exercise clearly signify improved performance of CMIP6 GCMs over CMIP5. Multi-model ensemble mean corresponding to the four emission scenarios are constructed using the best performing models of CMIP6 family. The study reveals that near-future changes in wind speed in the BoB are moderate under the low-end scenario of SSP1-2.6. Projected wind speeds in the head BoB are expected to increase or decrease by 20% during June–July–August and December–January–February under high-end scenario by the end of twenty-first century. A positive change up to 30% in the northeast monsoon winds under SSP5-8.5 is projected in the central BoB. Irrespective of the seasons, a net increase amounting to 0.6–0.8 m/s is observed along the east coast of India under SSP2-4.5 scenario by the mid and end of the century. Maximum rise by 25% (0.5–1 m/s) in wind speed is predicted under SSP3-7.0 scenario in the near future. Further, the study points out a decline in wind speed by 0.2–0.8 m/s in the central and southern BoB under the extreme scenario of SSP5-8.5. Strengthening and weakening of winds over the BoB accounts the projected variations in temperature that resulted from global warming and subsequent changes in atmospheric circulation.

Econometric Measurement of Earth's Transient Climate Sensitivity

How sensitive is Earth’s climate to a given increase in atmospheric greenhouse gas (GHG) concentrations? This long-standing and fundamental question in climate science was recently analyzed by dynamic panel data methods using extensive spatiotemporal data of global surface temperatures, solar radiation, and GHG concentrations over the last half century to 2010 (Storelvmo et al, 2016). These methods revealed that atmospheric aerosol effects masked approximately one-third of the continental warming due to increasing GHG concentrations over this period, thereby implying greater climate sensitivity to GHGs than previously thought. The present study provides asymptotic theory justifying the use of these methods when there are stochastic process trends in both the global forcing variables, such as GHGs, and station-level trend effects from such sources as local aerosol pollutants. These asymptotics validate confidence interval construction for econometric measures of Earth’s transient climate sensitivity. The methods are applied to observational data and to data generated from three leading global climate models (GCMs) that are sampled spatio-temporally in the same way as the empirical observations. The findings indicate that estimates of transient climate sensitivity produced by these GCMs lie within empirically determined confidence limits but that the GCMs uniformly underestimate the effects of aerosol induced dimming effects. The analysis shows the potential of econometric methods to calibrate GCM performance against observational data and to reveal the respective sensitivity parameters (GHG and non-GHG related) governing GCM temperature trends.

Climate analogue and future appearance of novel climate in Southeast Asia

AbstractThis study identified the analogue locations of five big cities and the future appearance of novel climate in Southeast Asia (SEA) at the end of the 21st century under the Representative Concentration Pathways 8.5 (RCP8.5) and 4.5 (RCP4.5) scenarios. A modified version of an existing formulation to estimate climate distance is introduced, using the monthly means of temperature and precipitation from six regional climate experiments and from six global climate models (GCMs). Results showed that regional downscaling allowed a more accurate representation of temperature but displayed a higher variability in rainfall over SEA compared to the GCM performance. The ensemble mean (ENS) experiment had a relatively better performance compared to each individual experiment in representing the monthly time series of temperature and precipitation. The common tendency of climatic relocation towards warmer regions for the five big cities in SEA (Hanoi, Bangkok, Manila, Kuala Lumpur and Jakarta) was prominent with the regional ENS experiment. At the end of the 21st century, the ratio of novel climate areas over SEA, mainly located in low elevation, coastal, equatorial regions, and islands, was less than 2% under RCP4.5, but increased to 24 and 21% under RCP8.5 for the ensemble regional and global experiments, respectively.