2021 Vol. 38, No. 6
Display Method:
2021, 38(6): 896-904.
doi: 10.1007/s00376-021-1094-y
Abstract:
China experienced significant flooding in the summer of 2020 and multiple extreme cold surges during the winter of 2020/21. Additionally, the 2020 typhoon season had below average activity with especially quiet activity during the first half of the season in the western North Pacific (WNP). Sea surface temperature changes in the Pacific, Indian, and Atlantic Oceans all contributed to the heavy rainfall in China, but the Atlantic and Indian Oceans seem to have played dominant roles. Enhancement and movement of the Siberian High caused a wavier pattern in the jet stream that allowed cold polar air to reach southward, inducing cold surges in China. Large vertical wind shear and low humidity in the WNP were responsible for fewer typhoons in the first half of the typhoon season. Although it is known that global warming can increase the frequency of extreme weather and climate events, its influences on individual events still need to be quantified. Additionally, the extreme cold surge during 16–18 February 2021 in the United States shares similar mechanisms with the winter 2020/21 extreme cold surges in China.
China experienced significant flooding in the summer of 2020 and multiple extreme cold surges during the winter of 2020/21. Additionally, the 2020 typhoon season had below average activity with especially quiet activity during the first half of the season in the western North Pacific (WNP). Sea surface temperature changes in the Pacific, Indian, and Atlantic Oceans all contributed to the heavy rainfall in China, but the Atlantic and Indian Oceans seem to have played dominant roles. Enhancement and movement of the Siberian High caused a wavier pattern in the jet stream that allowed cold polar air to reach southward, inducing cold surges in China. Large vertical wind shear and low humidity in the WNP were responsible for fewer typhoons in the first half of the typhoon season. Although it is known that global warming can increase the frequency of extreme weather and climate events, its influences on individual events still need to be quantified. Additionally, the extreme cold surge during 16–18 February 2021 in the United States shares similar mechanisms with the winter 2020/21 extreme cold surges in China.
2021, 38(6): 905-917.
doi: 10.1007/s00376-021-0222-z
Abstract:
Freshwater is recharged mainly by rainfall and stored inland for a period of time, which is directly affected by its storage capability. The storage capability of river basins has different spatiotemporal features that are important for the predictability of freshwater resources. However, the estimation of freshwater storage capability (FSC) remains a challenge due to the lack of observations and quantification indices. Here, we use a metric that characterizes hydrological “inertia” after rainfalls to analyze FSC over the 194 largest global major river basins based on satellite observations from the Gravity Recovery and Climate Experiment (GRACE) and simulations from the Community Land Model version 5 (CLM5). During 2003–16, the global land was observed to retain 28% of precipitation after one month based on GRACE observations, and the simulation depicts that the retained proportions decrease from 42% after one day to 26% after one month, with smaller FSC partly attributed to wetter conditions and higher vegetation densities. The root zone contributes about 40% to the global land FSC on daily to monthly time scales. As the time scale increases, the contribution from the surface soil decreases from 26% to 14%, while the contribution from the deep soil increases from 4% to 10%. Snow contributes over 20% of land FSC, especially over high latitudes. With six decades of CLM5 long-term simulations, it is revealed that the change of FSC in most basins is related to internal climate variability. The FSC of river basins which displays the proportion of precipitation retained on land is worthy of further attention regarding the predictability of water resources.
Freshwater is recharged mainly by rainfall and stored inland for a period of time, which is directly affected by its storage capability. The storage capability of river basins has different spatiotemporal features that are important for the predictability of freshwater resources. However, the estimation of freshwater storage capability (FSC) remains a challenge due to the lack of observations and quantification indices. Here, we use a metric that characterizes hydrological “inertia” after rainfalls to analyze FSC over the 194 largest global major river basins based on satellite observations from the Gravity Recovery and Climate Experiment (GRACE) and simulations from the Community Land Model version 5 (CLM5). During 2003–16, the global land was observed to retain 28% of precipitation after one month based on GRACE observations, and the simulation depicts that the retained proportions decrease from 42% after one day to 26% after one month, with smaller FSC partly attributed to wetter conditions and higher vegetation densities. The root zone contributes about 40% to the global land FSC on daily to monthly time scales. As the time scale increases, the contribution from the surface soil decreases from 26% to 14%, while the contribution from the deep soil increases from 4% to 10%. Snow contributes over 20% of land FSC, especially over high latitudes. With six decades of CLM5 long-term simulations, it is revealed that the change of FSC in most basins is related to internal climate variability. The FSC of river basins which displays the proportion of precipitation retained on land is worthy of further attention regarding the predictability of water resources.
2021, 38(6): 918-930.
doi: 10.1007/s00376-021-0348-z
Abstract:
Independent datasets consistently indicate a significant correlation between the sea ice variability in the Bering Sea during melt season and the summer rainfall variability in the Lake Baikal area and Northeastern China. In this study, four sea ice datasets (HadISST1, HadISST2.2, ERA-Interim and NOAA/NSIDC) and two global precipitation datasets (CRU V4.01 and GPCP V2.3) are used to investigate co-variations between melt season (March−April−May−June, MAMJ) Bering Sea ice cover (BSIC) and summer (June−July−August, JJA) East Asian precipitation. All datasets demonstrate a significant correlation between the MAMJ BSIC and the JJA rainfall in Lake Baikal−Northeastern China (Baikal−NEC). Based on the reanalysis datasets and the numerical sensitivity experiments performed in this study using Community Atmospheric Model version 5 (CAM5), a mechanism to understand how the MAMJ BSIC influences the JJA Baikal−NEC rainfall is suggested. More MAMJ BSIC triggers a wave train and causes a positive sea level pressure (SLP) anomaly over the North Atlantic during MAMJ. The high SLP anomaly, associated with an anti-cyclonic wind stress circulation anomaly, favors the appearance of sea surface temperature (SST) anomalies in a zonal dipole-pattern in the North Atlantic during summer. The dipole SST anomaly drives a zonally orientated wave train, which causes a high anomaly geopotential height at 500 hPa over the Sea of Japan. As a result, the mean East Asian trough moves westward and a low geopotential height anomaly occurs over Baikal−NEC. This prevailing regional low pressure anomaly together with enhanced moisture transport from the western North Pacific and convergence over Baikal−NEC, positively influences the increased rainfall in summer.
Independent datasets consistently indicate a significant correlation between the sea ice variability in the Bering Sea during melt season and the summer rainfall variability in the Lake Baikal area and Northeastern China. In this study, four sea ice datasets (HadISST1, HadISST2.2, ERA-Interim and NOAA/NSIDC) and two global precipitation datasets (CRU V4.01 and GPCP V2.3) are used to investigate co-variations between melt season (March−April−May−June, MAMJ) Bering Sea ice cover (BSIC) and summer (June−July−August, JJA) East Asian precipitation. All datasets demonstrate a significant correlation between the MAMJ BSIC and the JJA rainfall in Lake Baikal−Northeastern China (Baikal−NEC). Based on the reanalysis datasets and the numerical sensitivity experiments performed in this study using Community Atmospheric Model version 5 (CAM5), a mechanism to understand how the MAMJ BSIC influences the JJA Baikal−NEC rainfall is suggested. More MAMJ BSIC triggers a wave train and causes a positive sea level pressure (SLP) anomaly over the North Atlantic during MAMJ. The high SLP anomaly, associated with an anti-cyclonic wind stress circulation anomaly, favors the appearance of sea surface temperature (SST) anomalies in a zonal dipole-pattern in the North Atlantic during summer. The dipole SST anomaly drives a zonally orientated wave train, which causes a high anomaly geopotential height at 500 hPa over the Sea of Japan. As a result, the mean East Asian trough moves westward and a low geopotential height anomaly occurs over Baikal−NEC. This prevailing regional low pressure anomaly together with enhanced moisture transport from the western North Pacific and convergence over Baikal−NEC, positively influences the increased rainfall in summer.
2021, 38(6): 931-942.
doi: 10.1007/s00376-021-0339-0
Abstract:
This study investigates the circulation and precipitation anomalies associated with different configurations of the western North Pacific subtropical high (WNPSH) and the East Asian westerly jet (EAJ) in summer on interannual timescales. The in-phase configuration of the WNPSH and EAJ is characterized by the westward (eastward) extension of the WNPSH and the southward (northward) shift of the EAJ, which is consistent with the general correspondence between their variations. The out-of-phase configuration includes the residual cases. We find that the in-phase configuration manifests itself as a typical meridional teleconnection. For instance, there is an anticyclonic (cyclonic) anomaly over the tropical western North Pacific and a cyclonic (anticyclonic) anomaly over the mid-latitudes of East Asia in the lower troposphere. These circulation anomalies are more conducive to rainfall anomalies over the Yangtze River basin and south Japan than are the individual WNPSH or EAJ. By contrast, for the out-of-phase configuration, the mid-latitude cyclonic (anticyclonic) anomaly is absent, and the lower-tropospheric circulation anomalies feature an anticyclonic (cyclonic) anomaly with a large meridional extension. Correspondingly, significant rainfall anomalies move northward to North China and the northern Korean Peninsula. Further results indicate that the out-of-phase configuration is associated with the developing phase of ENSO, with strong and significant sea surface temperature (SST) anomalies in the tropical central and eastern Pacific which occur simultaneously during summer and persist into the following winter. This is sharply different from the in-phase configuration, for which the tropical SSTs are not a necessity.
This study investigates the circulation and precipitation anomalies associated with different configurations of the western North Pacific subtropical high (WNPSH) and the East Asian westerly jet (EAJ) in summer on interannual timescales. The in-phase configuration of the WNPSH and EAJ is characterized by the westward (eastward) extension of the WNPSH and the southward (northward) shift of the EAJ, which is consistent with the general correspondence between their variations. The out-of-phase configuration includes the residual cases. We find that the in-phase configuration manifests itself as a typical meridional teleconnection. For instance, there is an anticyclonic (cyclonic) anomaly over the tropical western North Pacific and a cyclonic (anticyclonic) anomaly over the mid-latitudes of East Asia in the lower troposphere. These circulation anomalies are more conducive to rainfall anomalies over the Yangtze River basin and south Japan than are the individual WNPSH or EAJ. By contrast, for the out-of-phase configuration, the mid-latitude cyclonic (anticyclonic) anomaly is absent, and the lower-tropospheric circulation anomalies feature an anticyclonic (cyclonic) anomaly with a large meridional extension. Correspondingly, significant rainfall anomalies move northward to North China and the northern Korean Peninsula. Further results indicate that the out-of-phase configuration is associated with the developing phase of ENSO, with strong and significant sea surface temperature (SST) anomalies in the tropical central and eastern Pacific which occur simultaneously during summer and persist into the following winter. This is sharply different from the in-phase configuration, for which the tropical SSTs are not a necessity.
2021, 38(6): 943-956.
doi: 10.1007/s00376-021-0316-7
Abstract:
The partial cycle (PC) strategy has been used in many rapid refresh cycle systems (RRC) for regional short-range weather forecasting. Since the strategy periodically reinitializes the regional model (RM) from the global model (GM) forecasts to correct the large-scale drift, it has replaced the traditional full cycle (FC) strategy in many RRC systems. However, the extra spin-up in the PC strategy increases the computer burden on RRC and generates discontinuous small-scale systems among cycles. This study returns to the FC strategy but with initial fields generated by dynamic blending (DB) and data assimilation (DA). The DB ingests the time-varied large-scale information from the GM to the RM to generate less-biased background fields. Then the DA is performed. We applied the new FC strategy in a series of 7-day batch forecasts with the 3-hour cycle in July 2018, and February, April, and October 2019 over China using a Weather Research and Forecast (WRF) model-based RRC. A comparison shows that the new FC strategy results in less model bias than the PC strategy in most state variables and improves the forecast skills for moderate and light precipitation. The new FC strategy also allows the model to reach a balanced state earlier and gives favorable forecast continuity between adjacent cycles. Hence, this new FC strategy has potential to be applied in RRC forecast systems to replace the currently used PC strategy.
The partial cycle (PC) strategy has been used in many rapid refresh cycle systems (RRC) for regional short-range weather forecasting. Since the strategy periodically reinitializes the regional model (RM) from the global model (GM) forecasts to correct the large-scale drift, it has replaced the traditional full cycle (FC) strategy in many RRC systems. However, the extra spin-up in the PC strategy increases the computer burden on RRC and generates discontinuous small-scale systems among cycles. This study returns to the FC strategy but with initial fields generated by dynamic blending (DB) and data assimilation (DA). The DB ingests the time-varied large-scale information from the GM to the RM to generate less-biased background fields. Then the DA is performed. We applied the new FC strategy in a series of 7-day batch forecasts with the 3-hour cycle in July 2018, and February, April, and October 2019 over China using a Weather Research and Forecast (WRF) model-based RRC. A comparison shows that the new FC strategy results in less model bias than the PC strategy in most state variables and improves the forecast skills for moderate and light precipitation. The new FC strategy also allows the model to reach a balanced state earlier and gives favorable forecast continuity between adjacent cycles. Hence, this new FC strategy has potential to be applied in RRC forecast systems to replace the currently used PC strategy.
2021, 38(6): 957-965.
doi: 10.1007/s00376-021-0404-8
Abstract:
In the study of diagnosing climate simulations and understanding the dynamics of precipitation extremes, it is an essential step to adopt a simple model to relate water vapor condensation and precipitation, which occur at cloud-microphysical and convective scales, to large-scale variables. Several simple models have been proposed; however, improvement is still needed in both their accuracy and/or the physical basis. Here, we propose a two-plume convective model that takes into account the subgrid inhomogeneity of precipitation extremes. The convective model has three components, i.e., cloud condensation, rain evaporation, and environmental descent, and is built upon the zero-buoyancy approximation and guidance from the high-resolution reanalysis. Evaluated against the CMIP5 climate simulations, the convective model shows large improvements in reproducing precipitation extremes compared to previously proposed models. Thus, the two-plume convective model better captures the main physical processes and serves as a useful diagnostic tool for precipitation extremes.
In the study of diagnosing climate simulations and understanding the dynamics of precipitation extremes, it is an essential step to adopt a simple model to relate water vapor condensation and precipitation, which occur at cloud-microphysical and convective scales, to large-scale variables. Several simple models have been proposed; however, improvement is still needed in both their accuracy and/or the physical basis. Here, we propose a two-plume convective model that takes into account the subgrid inhomogeneity of precipitation extremes. The convective model has three components, i.e., cloud condensation, rain evaporation, and environmental descent, and is built upon the zero-buoyancy approximation and guidance from the high-resolution reanalysis. Evaluated against the CMIP5 climate simulations, the convective model shows large improvements in reproducing precipitation extremes compared to previously proposed models. Thus, the two-plume convective model better captures the main physical processes and serves as a useful diagnostic tool for precipitation extremes.
2021, 38(6): 966-982.
doi: 10.1007/s00376-021-0287-8
Abstract:
The presence of embedded convection in stratiform clouds strongly affects ice microphysical properties and precipitation formation. In situ aircraft measurements, including upward and downward spirals and horizontal penetrations, were performed within both embedded convective cells and stratiform regions of a mixed-phase stratiform cloud system on 22 May 2017. Supercooled liquid water measurements, particle size distributions, and particle habits in different cloud regions were discussed with the intent of characterizing the riming process and determining how particle size distributions vary from convective to stratiform regions. Significant amounts of supercooled liquid water, with maxima up to 0.6 g m−3, were observed between −3°C and −6°C in the embedded convective cells while the peak liquid water content was generally less than 0.1 g m−3 in the stratiform regions. There are two distinct differences in particle size distributions between convective and stratiform regions. One difference is the significant shift toward larger particles from upper −15°C to lower −10°C in the convective region, with the maximum particle dimensions increasing from less than 6000 μm to over 1 cm. The particles larger than 1 cm at −10°C are composed of dendrites and their aggregates. The other difference is the large concentrations of small particles (25–205 μm) at temperatures between −3°C and −5°C in the convective region, where rimed ice particles and needles coexist. Needle regions are observed from three of the five spirals, but only the cloud conditions within the convective region fit into the Hallett-Mossop criteria.
2021, 38(6): 983-993.
doi: 10.1007/s00376-021-0252-6
Abstract:
The impact of assimilating radiance data from the advanced satellite sensor GMI (GPM microwave imager) for typhoon analyses and forecasts was investigated using both a three-dimensional variational (3DVAR) and a hybrid ensemble-3DVAR method. The interface of assimilating the radiance for the sensor GMI was established in the Weather Research and Forecasting (WRF) model. The GMI radiance data are assimilated for Typhoon Matmo (2014), Typhoon Chan-hom (2015), Typhoon Meranti (2016), and Typhoon Mangkhut (2018) in the Pacific before their landing. The results show that after assimilating the GMI radiance data under clear sky condition with the 3DVAR method, the wind, temperature, and humidity fields are effectively adjusted, leading to improved forecast skills of the typhoon track with GMI radiance assimilation. The hybrid DA method is able to further adjust the location of the typhoon systematically. The improvement of the track forecast is even more obvious for later forecast periods. In addition, water vapor and hydrometeors are enhanced to some extent, especially with the hybrid method.
The impact of assimilating radiance data from the advanced satellite sensor GMI (GPM microwave imager) for typhoon analyses and forecasts was investigated using both a three-dimensional variational (3DVAR) and a hybrid ensemble-3DVAR method. The interface of assimilating the radiance for the sensor GMI was established in the Weather Research and Forecasting (WRF) model. The GMI radiance data are assimilated for Typhoon Matmo (2014), Typhoon Chan-hom (2015), Typhoon Meranti (2016), and Typhoon Mangkhut (2018) in the Pacific before their landing. The results show that after assimilating the GMI radiance data under clear sky condition with the 3DVAR method, the wind, temperature, and humidity fields are effectively adjusted, leading to improved forecast skills of the typhoon track with GMI radiance assimilation. The hybrid DA method is able to further adjust the location of the typhoon systematically. The improvement of the track forecast is even more obvious for later forecast periods. In addition, water vapor and hydrometeors are enhanced to some extent, especially with the hybrid method.
2021, 38(6): 994-1011.
doi: 10.1007/s00376-021-0062-x
Abstract:
The development and evolution of precipitation microphysical parameters and the vertical structure characteristics associated with Typhoon Yagi (201814) are analyzed in the city of Jinan, Shandong Province based primarily on the observations of a micro rain radar (MRR), a cloud radar, and a disdrometer. The precipitation process is further subdivided into four types: convective, stratiform, mixed, and light precipitation according to the ground disdrometer data, which is in agreement with the vertical profile of the radar reflectivity detected by the MRR. Vertical winds may be the main source of MRR retrieval error during convective precipitation. Convective precipitation has the shortest duration but makes the largest contribution to the cumulative precipitation. Collision-coalescence is the main microphysical process of stratiform precipitation and light precipitation below the bright band observed by the MRR. It is worth noting that as Typhoon Yagi (201814) transformed into an extratropical cyclone, its raindrop size distributions no longer had the characteristics of maritime precipitation, but become more typical of the characteristic of continental precipitation, which represents a very different raindrop size distribution from that which is normally observed in a landfalling typhoon.
The development and evolution of precipitation microphysical parameters and the vertical structure characteristics associated with Typhoon Yagi (201814) are analyzed in the city of Jinan, Shandong Province based primarily on the observations of a micro rain radar (MRR), a cloud radar, and a disdrometer. The precipitation process is further subdivided into four types: convective, stratiform, mixed, and light precipitation according to the ground disdrometer data, which is in agreement with the vertical profile of the radar reflectivity detected by the MRR. Vertical winds may be the main source of MRR retrieval error during convective precipitation. Convective precipitation has the shortest duration but makes the largest contribution to the cumulative precipitation. Collision-coalescence is the main microphysical process of stratiform precipitation and light precipitation below the bright band observed by the MRR. It is worth noting that as Typhoon Yagi (201814) transformed into an extratropical cyclone, its raindrop size distributions no longer had the characteristics of maritime precipitation, but become more typical of the characteristic of continental precipitation, which represents a very different raindrop size distribution from that which is normally observed in a landfalling typhoon.
2021, 38(6): 1012-1019.
doi: 10.1007/s00376-021-0251-7
Abstract:
Weather manifests in spatiotemporally coherent structures. Weather forecasts hence are affected by both positional and structural or amplitude errors. This has been long recognized by practicing forecasters (cf., e.g., Tropical Cyclone track and intensity errors). Despite the emergence in recent decades of various objective methods for the diagnosis of positional forecast errors, most routine verification or statistical post-processing methods implicitly assume that forecasts have no positional error. The Forecast Error Decomposition (FED) method proposed in this study uses the Field Alignment technique which aligns a gridded forecast with its verifying analysis field. The total error is then partitioned into three orthogonal components: (a) large scale positional, (b) large scale structural, and (c) small scale error variance. The use of FED is demonstrated over a month-long MSLP data set. As expected, positional errors are often characterized by dipole patterns related to the displacement of features, while structural errors appear with single extrema, indicative of magnitude problems. The most important result of this study is that over the test period, more than 50% of the total mean sea level pressure forecast error variance is associated with large scale positional error. The importance of positional error in forecasts of other variables and over different time periods remain to be explored.
2021, 38(6): 1020-1027.
doi: 10.1007/s00376-021-0238-4
Abstract:
Convective/large-scale (C/L) precipitation partitions are crucial for achieving realistic rainfall modeling and are classified in 16 phase 6 of the Coupled Model Intercomparison Project (CMIP6) atmospheric models. Only 4 models capture the feature that convective rainfall significantly exceeds the large-scale rainfall component in the tropics while the other 12 models show 50%–100% large-scale rainfall component in heavy rainfall. Increased horizontal resolution generally increases the convective rainfall percentage, but not in all models. The former 4 models can realistically reproduce two peaks of moisture vertical distribution, respectively located in the upper and the lower troposphere. In contrast, the latter 12 models correspond to three types of moisture vertical profile biases: (1) whole mid-to-lower tropospheric wet biases (60%–80% large-scale rainfall); (2) mid-tropospheric wet peak (50% convective/large-scale rainfall); and (3) lower-tropospheric wet peak (90%–100% large-scale rainfall). And the associated vertical distribution of unique clouds potentially causes different climate feedback, suggesting accurate C/L rainfall components are necessary to reliable climate projection.
Convective/large-scale (C/L) precipitation partitions are crucial for achieving realistic rainfall modeling and are classified in 16 phase 6 of the Coupled Model Intercomparison Project (CMIP6) atmospheric models. Only 4 models capture the feature that convective rainfall significantly exceeds the large-scale rainfall component in the tropics while the other 12 models show 50%–100% large-scale rainfall component in heavy rainfall. Increased horizontal resolution generally increases the convective rainfall percentage, but not in all models. The former 4 models can realistically reproduce two peaks of moisture vertical distribution, respectively located in the upper and the lower troposphere. In contrast, the latter 12 models correspond to three types of moisture vertical profile biases: (1) whole mid-to-lower tropospheric wet biases (60%–80% large-scale rainfall); (2) mid-tropospheric wet peak (50% convective/large-scale rainfall); and (3) lower-tropospheric wet peak (90%–100% large-scale rainfall). And the associated vertical distribution of unique clouds potentially causes different climate feedback, suggesting accurate C/L rainfall components are necessary to reliable climate projection.
2021, 38(6): 1028-1049.
doi: 10.1007/s00376-021-0343-4
Abstract:
Large-ensemble simulations of the atmosphere-only time-slice experiments for the Polar Amplification Model Intercomparison Project (PAMIP) were carried out by the model group of the Chinese Academy of Sciences (CAS) Flexible Global Ocean-Atmosphere-Land System (FGOALS-f3-L). Eight groups of experiments forced by different combinations of the sea surface temperature (SST) and sea ice concentration (SIC) for pre-industrial, present-day, and future conditions were performed and published. The time-lag method was used to generate the 100 ensemble members, with each member integrating from 1 April 2000 to 30 June 2001 and the first two months as the spin-up period. The basic model responses of the surface air temperature (SAT) and precipitation were documented. The results indicate that Arctic amplification is mainly caused by Arctic SIC forcing changes. The SAT responses to the Arctic SIC decrease alone show an obvious increase over high latitudes, which is similar to the results from the combined forcing of SST and SIC. However, the change in global precipitation is dominated by the changes in the global SST rather than SIC, partly because tropical precipitation is mainly driven by local SST changes. The uncertainty of the model responses was also investigated through the analysis of the large-ensemble members. The relative roles of SST and SIC, together with their combined influence on Arctic amplification, are also discussed. All of these model datasets will contribute to PAMIP multi-model analysis and improve the understanding of polar amplification.
Large-ensemble simulations of the atmosphere-only time-slice experiments for the Polar Amplification Model Intercomparison Project (PAMIP) were carried out by the model group of the Chinese Academy of Sciences (CAS) Flexible Global Ocean-Atmosphere-Land System (FGOALS-f3-L). Eight groups of experiments forced by different combinations of the sea surface temperature (SST) and sea ice concentration (SIC) for pre-industrial, present-day, and future conditions were performed and published. The time-lag method was used to generate the 100 ensemble members, with each member integrating from 1 April 2000 to 30 June 2001 and the first two months as the spin-up period. The basic model responses of the surface air temperature (SAT) and precipitation were documented. The results indicate that Arctic amplification is mainly caused by Arctic SIC forcing changes. The SAT responses to the Arctic SIC decrease alone show an obvious increase over high latitudes, which is similar to the results from the combined forcing of SST and SIC. However, the change in global precipitation is dominated by the changes in the global SST rather than SIC, partly because tropical precipitation is mainly driven by local SST changes. The uncertainty of the model responses was also investigated through the analysis of the large-ensemble members. The relative roles of SST and SIC, together with their combined influence on Arctic amplification, are also discussed. All of these model datasets will contribute to PAMIP multi-model analysis and improve the understanding of polar amplification.
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