Seasonal Mean Temperature Research Articles

Evaluation of Temperature and Precipitation Simulations in CMIP6 Models Over the Tibetan Plateau

AbstractThis study presents a comprehensive evaluation of the mean and extreme surface air temperature and precipitation in the Coupled Model Intercomparison Project Phase 6 (CMIP6) multimodel ensemble simulations over the Tibetan Plateau. Simulations from 29 climate models are compared against gridded observations for the period 1961–2012. Results show that models reasonably reproduce the overall spatial patterns of the 1961–2012 averages of seasonal mean temperature and precipitation. Nevertheless, models tend to underestimate mean temperature and overestimate precipitation accumulations. To be specific, the multimodel mean estimates of seasonal average temperatures over the plateau are 0.5°C–2.6°C colder than observed, while the corresponding estimates of seasonal precipitation accumulations are 218% (spring: March–May), 76% (summer: June–August), 129% (autumn: September–November), and 533% (winter: December–February) of those observed. As in observations, models also well capture increasing trend of mean temperature and precipitation in all seasons but underestimate the rates of increasing trends for both variables in all seasons. Models’ ability to simulate temperature and precipitation extremes is also evaluated in terms of a set of chosen extreme indices defined by the Expert Team on Climate Change Detection and Indices. On average, models tend to underestimate the averages of annual maximum daily maximum temperature, annual minimum daily minimum temperature, frost days, warm nights, and consecutive dry days but overestimate the corresponding averages of annual maximum 5‐day precipitation accumulations and simply daily intensity. Generally, models are able to simulate the signs of the trends in extreme indices but underestimate their magnitudes and misrepresent spatial patterns.

Relationship between the interannual and intraseasonal temperature variability in Northeast China

AbstractA significant negative correlation between the seasonal mean temperature anomaly and the intensity of the intraseasonal temperature oscillation in the northern winter over Northeast China is found during the time period 1959–2011. A stronger intraseasonal oscillation (ISO) is found during an extremely cold winter. The cold winter in Northeast China is a part of zonally oriented large‐scale pattern with a negative (positive) temperature anomaly in mid‐ (high) latitudes and is associated with a negative phase of the North Atlantic Oscillation (NAO). The intraseasonal temperature anomaly originates from high‐latitude Central Eurasia and propagates southeastward. The southeastward propagation is attributed to the upper tropospheric dispersion of Rossby wave energy as a result of coupling of the low‐level temperature and the upper‐level geopotential height. The cause of the negative correlation between the intraseasonal and interannual modes is investigated. A cold winter in Northeast China is associated with a weakened meridional temperature gradient and vertical wind shear north of 50°N. This weakens synoptic variability in situ through baroclinic instability. Because the ISO competes for an energy source with the synoptic motion, the ISO is enhanced north of 50°N. The southeastward propagation of the enhanced ISO mode increased intraseasonal temperature variability over Northeast China. As a result, a cold winter in Northeast China is accompanied by an enhanced ISO variability in situ. The enhanced local ISO could further feedback to the winter mean temperature anomaly through nonlinear advective processes. The diagnosis of the temperature budget indicates that the ISO may interact with motion on other scales to contribute to the winter mean temperature anomaly in situ.

Trend and change point detection in mean annual and seasonal maximum temperatures over Saudi Arabia

Trend and change point detection in mean annual and seasonal maximum temperatures over Saudi Arabia

Added value in dynamically downscaling seasonal mean temperature simulations over West Africa

The present study proposes an assessment of the Added Value (AV) in seasonal mean temperature regional simulations by the Rossby Centre Regional Climate Atmospheric Model version 4 (RCA4) over West Africa, with emphasize on the contribution the driving Global Climate Models (GCMs) and the downscaling experiment. The results show that the AV by RCA4 varies depending on the driven GCMs, the season, and subareas of the study domain, while consistent performances are observed for specific GCMs. These results suggest that the key influencing factor of the quality of GCM driven RCA4 simulations is the consistencies between the GCMs and the RCA4 model physics. Our findings also point out that the RCA4's contribution to the AV results is influential, as it highly compensates the driven GCMs biases, although positive AV results are typically GCM dependent.

Agricultural perspectives on the 2015-2018 Western Cape drought, South Africa: Characteristics and spatial variability in the core wheat growing regions

Between 2015 and 2018, the Western Cape Province of South Africa experienced a multi-year severe drought, which negatively impacted major economic sectors. The province serves as an important producer of wheat in South Africa among other agricultural commodities. This study aims to analyze the 2015-2018 drought and its impacts on wheat production in the winter rainfall region of the Western Cape, South Africa. The central focus is to place the drought in both a historical and future context while emphasising the differences between the two core wheat growing regions. We present an analysis of the drought, as well as climate trends computed from weather data sets (1988–2018) from six weather stations across the two core wheat-growing. We first present a meteorological drought analysis of mean annual and seasonal rainfall and temperatures, subsequently providing an agricultural lens by computing Standardised Precipitation and Evapotranspiration Index (SPEI) accumulated over 12- and 36-month timescales, self-calibrated Palmer Drought Severity Index (sc-PDSI), changes to the start and end of the rainfall season, and their effects on wheat yields. Trend analysis was conducted to determine if drought observations form part of the longer-term trends in the region. Finally, we show how the drought varied spatially across the two regions. Results show that between 1988 and 2018, the wheat growing areas of the Western Cape experienced persistent drought with high spatial-temporal variability. The 2015-2018 drought, however, was the most severe experienced in the 30-year study period at five of the six stations. These results are consistent with conditions that can be expected under future climate change. Moreover, results can be useful for the development of early warning systems since they place the drought in the context of past drought conditions.

Assessment of Temperature Changes on the Tibetan Plateau During 1980–2018

AbstractSurface air temperatures affect a diverse set of physical and biological systems in many parts of the world. For regional‐scale studies, gridded surface air temperature data sets are frequently used as input variables. Here we evaluate 10 commonly used gridded air temperature products with spatial resolutions ranging from 0.1° × 0.1° to 5.0° × 5.0°, relative to observations from in situ weather stations on the Tibetan Plateau. Gridded temperatures are consistently lower, with mean annual air temperature biases ranging from −4.68 °C to −1.72 °C, and root mean square error (RMSE) from 3.24 °C to 6.11 °C. The mean biases of mean seasonal air temperatures for spring, summer, autumn, and winter are −3.21 °C, −2.98 °C, −2.89 °C, and −2.98 °C, respectively, and RMSEs of 4.81 °C, 4.39 °C, 4.3 °C, and 4.7 °C. Of all 10 products, the China meteorological forcing data set compares best with observations. Annual and season temperatures all increase significantly during 1980–2018 on the Tibetan Plateau, with winter dominating the warming rate.

Comparative Analysis between Daily Extreme Temperature and Precipitation Values Derived from Observations and Gridded Datasets in North-Western Romania

Climate gridded datasets are highly needed and useful in conducting data analysis for research and practical purposes. They provide long-term information on various climatic variables for large areas worldwide, making them suitable for use at any spatial level. It is essential to assess the accuracy of gridded data by comparing the data to measured values, especially when they are used as input parameters for hydro-climatic models. From the multitude of databases available for North-western Romania, we selected three, particularly the European Climate Assessment and Dataset (E-OBS), the Romanian Climatic Dataset (ROCADA), and the Climate of the Carpathian Region (CARPATCLIM). In this paper, we analyse the extreme precipitation and temperature data derived from the above-mentioned datasets over a common 50-year period (1961–2010) and compare the data with raw values to estimate the degree of uncertainty for each set of data. The observation data, recorded at two meteorological stations located in a complex topography region, were compared to the output of the gridded datasets, by using descriptive statistics for the mean and extreme annual and seasonal temperature and precipitation data, and trend analyses. The main findings are: the high suitability of the ROCADA gridded database for climate trend analysis and of the E-OBS gridded database for extreme temperature and precipitation analysis.

A synthetic analysis of the effect of water and nitrogen inputs on wheat yield and water- and nitrogen-use efficiencies in China

Water and nitrogen (N) inputs are essential for increasing crop yields, but overuse inevitably causes substantial nitrate leaching and the subsequent contamination of groundwater. Water and N use in wheat production in China is relatively inefficient, so a synthesis of data for the effects of water and N inputs on wheat yield, water-use efficiency (WUE) and nitrogen-use efficiency (NUE) is necessary. We conducted a meta-analysis based on 4162 yield, 4158 WUE and 3694 NUE observations from 332 peer-reviewed publications to quantify the effects of water and N inputs on wheat yield, WUE and NUE and to clarify the potentials for saving water and N for wheat production in China. The positive effects of water or N input on all variables decreased when the optimum levels of input were exceeded. Actual yield, WUE and NUE were 29.8, 47.2 and 41.8% significantly lower, respectively, than those potentially attainable. Results differed greatly among regions due to differences in management and climate. The differences between actual and attainable yield, WUE and NUE were highest in the northwestern region and lowest in the north-central region. The difference in WUE was largest in the lower and middle reaches of the Yangtze River. The overall water- and N-saving potentials for wheat production across China were 22.4 and 26.0%, respectively. Yield was highest at a plant density of 180 plants m−2. The effects of the water and N inputs were more pronounced in areas with silty soil and high levels of soil organic carbon. Yield, WUE and NUE were negatively correlated with mean seasonal temperature, so suitable measures should be taken to control temperatures <15 °C. This analysis provides scientific guidance for improving the use efficiencies of resources, reducing environmental pollution, advancing China’s food security and improving yield stability.

Local Climatic Factors Mediated Impacts of Large-Scale Climate Oscillations on the Growth of Vegetation Across the Tibetan Plateau

Large-scale climate oscillations, particularly the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO), have widespread influences on climate systems across the Tibetan Plateau (TP). It is understudied how the temporal changes in AMO and PDO affected growth of vegetation through modifying the local climatic factors in different areas across the TP. We used the AMO and PDO indices, gridded growing season mean temperature (TGS), cumulative precipitation (PGS), and normalized difference vegetation index (NDVIGS) data from 1982 to 2015 to investigate the temporal trends of these variables and the correlations of the TGS and PGS with each of the AMO and PDO indices as well as their correlations with the NDVIGS. The results showed that the warming of the TGS over the TP and the increases of the PGS in western, central, and northeastern areas of the TP may have been related to an increase of the AMO index and a decrease of the PDO index. Combining those relationships with the spatial patterns of the TGS-NDVIGS and PGS-NDVIGS correlations suggested that the changes of the AMO and PDO may have indirectly increased the NDVIGS in the central and northeastern areas of the TP by increasing TGS and PGS, in most parts of the southwestern TP by increasing PGS, and in the eastern and south-central regions of the TP by increasing TGS. In contrast, the decrease of the NDVIGS in some areas of the southeastern and southwestern TP may have been associated with a negative effect of warming as a result of changes in the AMO and PDO. These results highlight the indirect impacts of changes in large-scale climate oscillations on the growth of vegetation through modification of local climatic factors across the TP, and they suggest the substantial spatial heterogeneity of these impacts largely depends on the responses of vegetation to local climatic factors.

Investigating the WRF Temperature and Precipitation Performance Sensitivity to Spatial Resolution over Central Europe

The grid size resolution effect on the annual and seasonal simulated mean, maximum and minimum daily temperatures and precipitation is assessed using the Advanced Research Weather Research and Forecasting model (ARW-WRF, hereafter WRF) that dynamically downscales the National Centers for Environmental Prediction’s final (NCEP FNL) Operational Global Analysis data. Simulations were conducted over central Europe for the year 2015 using 36, 12 and 4 km grid resolutions. Evaluation is done using daily E-OBS data. Several performance metrics and the bias adjusted equitable threat score (BAETS) for precipitation are used. Results show that model performance for mean, maximum and minimum temperature improves when increasing the spatial resolution from 36 to 12 km, with no significant added value when further increasing it to 4 km. Model performance for precipitation is slightly worsened when increasing the spatial resolution from 36 to 12 km while further increasing it to 4 km has minor effect. However, simulated and observed precipitation data are in quite good agreement in areas with precipitation rates below 3 mm/day for all three grid resolutions. The annual mean fraction of observed and/or forecast events that were correctly predicted (BAETS), when increasing the grid size resolution from 36 to 12 and 4 km, suggests a slight modification on average over the domain. During summer the model presents significantly lower BAETS skill score compared to the rest of the seasons.

Seasonal temperature trends on the Spanish mainland: A secular study (1916–2015)

AbstractTrends in seasonal mean values of maximum and minimum temperature are analysed in the Spanish mainland from the new MOTEDAS_century database. This new data set has been developed combining the digitalized archives from the Spanish Meteorological Agency (AEMET) with information retrieved from Annual Books published by the former Meteorological Agency dating back to 1916, and covers the period 1916–2015. In all four seasons, mean seasonal temperature of maximum (Tmax) and minimum (Tmin) increased. The raising occurred in two main pulses separated by a first pause around the middle of the 20th century, but differed among seasons and also between maximum and minimum temperature. Analysis of the percentage of land affected by significant trends in maximum temperature reveals two increasing phases in spring and summer for Tmax, and in spring, summer, and autumn for Tmin. However, winter Tmax only rose during the recent decades, and autumn Tmax in the first decades. Negative significant trends were found in extended areas in spring Tmax, and in spring, autumn, and summer Tmin, confirming the first pause around the 1940's–1960's. Trends of seasonal mean values of Tmax and Tmin are not significant for at least the last 25–35 years of the study period, depending on the season. The areas under significant positive trend are usually more extended for Tmin than Tmax at any season and period. Areas with significant trend expand and contract in time according to two spatial gradients: south‐east to north‐west (east‐west) for Tmax, and west to east for Tmin. We hypothesize a relationship between atmospheric prevalent advection and relief as triggering factors to understand spatial and temporal differences in seasonal temperatures at regional scale during the 20th century in the Iberian Peninsula.

Dynamics of Carbon Accumulation in Tropical Dry Forests under Climate Change Extremes

We analyze here how much carbon is being accumulated annually by secondary tropical dry forests (TDFs) and how structure, composition, time since abandonment, and climate can influence the dynamics of forest carbon accumulation. The study was carried out in Santa Rosa National Park in Guanacaste province, Costa Rica and Mata Seca State Park in Minas Gerais, Brazil. Total carbon storage and carbon accumulation were obtained for both sites from the sum of the aboveground carbon and belowground carbon gain plus the annual litterfall. Carbon accumulation of these TDFs varied from 2.6 Mg C ha−1 y−1 to 6.3 Mg C ha−1 y−1, depending on the age of the forest stands. Time since abandonment and number of stems per plot were the best predictors for carbon storage, annual carbon gains, and losses. Mortality rates and carbon losses were also associated with seasonal climate variability. We found significant correlations between tree mortality, carbon losses and mean seasonal temperature, mean seasonal precipitation, potential evapotranspiration, and the Oceanic Niño Index. Carbon dynamics in tropical dry forests are driven by time since abandonment and forest structure; however, rising temperature and El Niño Southern Oscillation (ENSO) events can have a significant impact on tree mortality and carbon losses. Depending on their location and land-use history, some dry forests are more impacted by climatic extremes than others, and differences between secondary stages are expected.

The trend of temperature of the Qilian Mountains in the Northwest of China

Temperature is one of the important climatological factors which affects the hydrological processes in a watershed, and is commonly used to investigate the change of climate. The distribution and trend of annual and seasonal mean temperature for 44 meteorological stations in Qilian Mountains over a period of 1960 to 2017 were analyzed by using linear regression. The mean annual temperature ranges from -2.8 to 9.7°C, and is above 0°C below middle mountains. However, the mean temperature of summer is maximum above 43.1°C, and is minimum in winter below -41.6°C. Vertical of mean annual temperature is distributed significantly. The distribution of mean annual temperature is higher in northern slope than in southern, and higher in northwest than in southeast. The warming trend is very strong in winter, and has a certain dependence on the altitude above the 37.5°of northern latitude, which is different in different seasons. The result of the present research would be helpful to promote ecological protection, adjustment of agricultural structure, and management of water resources in the Qilian Mountains.

Observational Constraints on the Response of High‐Latitude Northern Forests to Warming

AbstractSince the 1960s, carbon cycling in the high‐latitude northern forest (HLNF) has experienced dramatic changes: Most of the forest is greening and net carbon uptake from the atmosphere has increased. During the same time period, the CO2 seasonal cycle amplitude (SCA) has increased by ~50% or more. Disentangling complex processes that drive these changes has been challenging. In this study, we substitute spatial sensitivity to temperature for time to quantify the impact of temperature increase on gross primary production (GPP), total ecosystem respiration (TER), the fraction of Photosynthetic Active Radiation (fPAR), and the resulted contribution of these changes in amplifying the CO2 SCA over the HLNF since 1960s. We use the spatial heterogeneity of GPP inferred from solar‐induced chlorophyll Fluorescence in combination with net ecosystem exchange (NEE) inferred from column CO2 observations made between 2015 and 2017 from NASA's Orbiting Carbon Observatory‐2. We find that three quarters of the spatial variations in GPP can be explained by the spatial variation in the growing season mean temperature (GSMT). The long term hindcast captures both the magnitude and spatial variability of the trends in observed fPAR. We estimate that between 1960 and 2010, the increase in GSMT enhanced both GPP and the SCA of NEE by ~20%. The calculated enhancement of NEE due to increase in GSMT contributes 56–72% of the trend in the CO2 SCA at high latitudes, much larger than simulations by most biogeochemical models.

Characteristics of temperature evolution from 1960 to 2015 in the Three Rivers\u2019 Headstream Region, Qinghai, China

The cumulative anomaly analysis, the ensemble empirical mode decomposition (EEMD), the Bernaola Galvan heuristic segmentation algorithm (BGSA), the Le Page test, the moving t test at different sub-series scales, and the quasi-periodic oscillations (QPOs) were used to demonstrate the statistical characteristics of the temperature changes in the study area from 1960 to 2015. The results were as follows: the temperatures varied obviously among subregions and seasons and they generally increased; the climate tendency rates of autumn mean temperatures were higher than those of summer and spring; additionally, the temperatures in the three subregions of the Three Rivers’ Headstream Region (THRHR) were relatively low in the 1960s, especially in the early 1960s, followed by those in the 1970s, and the annual mean temperature has been increasing since the mid-late 1980s, especially in the middle 1990s. The results of EEMD showed that the QPOs of the annual mean temperature series in the study area were mainly quasi-3 years, quasi-5–8 years, quasi-12–15 years, and quasi-35–38 years. The results of the annual mean temperature series mutational sites showed that a significant warming mutation began in approximately 1997; and the mutational sites of seasonal mean temperature series in the three subregions of the THRHR all began in the middle and late 1990s. The prediction result of the temperature series trend based on multiple methods showed that the warming persistence of annual and seasonal mean temperature series would be stronger, and their seasonal and regional differences were obvious.

Climate changes in European Russia in 2001-2018

From the data of observations at 43 aerodromes in European Russia and the neighboring countries for 2001-2018, linear trends are calculated of temperature, humidity, and pressure, and then compared against the temporal changes in pressure field patterns. The latter are estimated on the basis of objective analysis data, using a special software. The linear trends show that the trends of annual-mean and season-mean temperature are positive practically at all the sites, though mean temperature in separate months can exhibit both decrease (in January) and strong increase (in December and summer months). The annual mean dew-point deficit increases in time almost everywhere, while surface pressure increases in the north (larger) part of the area and decreases in its south part. It is shown also, by means of objective classification of pressure field patterns, that the growth of cyclonic situations occurrence frequency dominates in the south part of the area, and the growth of anticyclonic situations occurrence frequency dominates in its main, north part. This result is consistent with the pressure trends distribution over the area.

A performance‐based multi‐model combination approach to reduce uncertainty in seasonal temperature change projections

AbstractFuture changes in climate variable exhibit prominent impact on flood magnitudes, crop yields, and freshwater withdrawal. Researchers typically ignore the large degree of uncertainty translated from climate projections to the estimated climate change magnitudes while applying pre‐processing approaches on climate change projections. General Circulation Models (GCM) exhibit substantial uncertainty in projecting future changes in the seasonal temperature, which is evaluated by estimating the shift in either the mean or variance. Bias between the observed changes (1950–1999) and GCM simulated changes vary across models, climate regions, seasons, and under emission scenarios. The simplest approach to reduce model structural uncertainty, equal weighting of GCMs, does not consider superiority of one or multiple GCMs compared to the rest. The current study adopts a performance‐based model combination approach that has shown efficiency in streamflow and weather forecasting, and GCM precipitation simulation. The optimal model combination approach has been modified to combine multi‐model climate change information, while yielding the spatial correlation in climate change information within a geographic region. The optimal model combination approach, along with a simple bias‐correction, is applied on 10 GCMs over nine climate regions across the coterminous United States (CONUS). We found that the optimal combination exhibits lower RMSE values as compared to the equal combination. Correlations between the model combined projections under optimal combination and the observed changes are strong and positive (&gt;0.5). Future (2000–49) model combined projections exhibit an increase in the mean seasonal temperature by 2°C for winter and by 1°C for summer over almost all climate regions.

Reduced stomatal frequency with rising elevation for Kobresia royleana on the Tibetan Plateau

Knowledge about within-species variation in stomatal frequency with varying elevation at very high elevations is rare, which is crucial for us to understand how alpine plants are adapted to the extreme environment. Here, we focus on the variation in stomatal frequency in Kobresia royleana (Nees) Boeckeler (Cyperaceae, Cyperales) along two altitudinal transects (elevation ranges from 3723 m to 5081 m) in the center of the Tibetan Plateau. The result shows the stomatal density (SD) varied from 303 ± 55.6 mm−2 to 542 ± 81.8 mm−2, and stomatal index (SI) ranged from 21.0% to 29.6%. In contrast with most cases, an unexpected negative response of stomatal frequency to rising elevation was observed. Among abiotic factors, the growing season mean temperature and CO2 partial pressure significantly declined with increasing elevation, while the growing season precipitation did not vary. Therefore, the decreasing SD and SI were mainly due to the declining temperature rather than the decreasing CO2 partial pressure. Further, SD and SI were negatively related to leaf functional traits of specific leaf area (SLA), leaf nitrogen concentration (N) and stable carbon isotope ratios (δ13C), and all these morphological and physiological traits tended to covary with rising elevation and declining temperature. Meanwhile, the increasing δ13C, N and SLA with elevation seem to be strategies for alpine plants to cope with the low-temperature environments. Therefore, the observed covariance between stomatal frequency and leaf functional traits also suggests that the low temperature rather than low CO2 partial pressure mainly leads to the elevational pattern of stomatal frequency for this alpine species.

Effect of Climate Variables in Rice Yield in Nueva Ecija, Philippines

Climate variability is one of the factors that directly and greatly affect cropping system and plant yield. It is therefore very important to obtain a good understanding about climate variability or changes in the climate and the effect of these changes to clearly understand the vulnerability of food crops as well as its agronomic impacts for us to create and implement adaptive strategies to mitigate its negative effects. This study assessed the effect of climate in rice crop yield in both irrigated and rainfed ecotype farming system in Nueva Ecija Province in the Philippines using semi-annual yield data and the different climate variables such as seasonal rainfall, mean temperature, minimum temperature, and relative humidity by using empirical/statistical method through time series analysis, and correlation analysis. Results indicated that rice yield for irrigated and rainfed ecosystem type of farming in Nueva Ecija show an overall increasing trend from year 1991-2018, although there are observed decline and fluctuations in some years. The different climate variables (i.e., rainfall, temperature, and humidity) show fluctuating trends and irregularities spanning from the year 1991-2018. But it showed overall decreasing trends for relative humidity and increasing trends for rainfall, minimum temperature, and mean temperature. There are significant correlations between rice yield the all the climate variables in both irrigated and rainfed farming ecosystem types.

Evaluation of Historical CMIP6 Model Simulations of Seasonal Mean Temperature over Pakistan during 1970–2014

This work employed recent model outputs from coupled model intercomparison project phase six to simulate surface mean temperature during the June–July–August (JJA) and December–January–February (DJF) seasons for 1970–2014 over Pakistan. The climatic research unit (CRU TS4.03) dataset was utilized as benchmark data to analyze models’ performance. The JJA season exhibited the highest mean temperature, whilst DJF displayed the lowest mean temperature in the whole study period. The JJA monthly empirical cumulative distribution frequency (ECDF) range (26 to 28 °C) was less than that of DJF (7 to 10 °C) since JJA matched closely to CRU. The JJA and DJF seasons are warming, with higher warming trends in winters than in summers. On temporal scale, models performed better in JJA with overall low bias, low RMSE (root mean square error), and higher positive CC (correlation coefficient) values. DJF performance was undermined with higher bias and RMSE with weak positive correlation estimates. Overall, CanESM5, CESM2, CESM2-WACCM, GFDL-CM4, HadGEM-GC31-LL, MPI-ESM1-2-LR, MPI-ESM1-2-HR, and MRI-ESM-0 performed better for JJA and DJF.