Surface Air Temperature Research Articles

Interannual variation of the westward ridge point of the Western Pacific subtropical high in boreal winter

This study adopts a novel dynamic index of the westward ridge point (WRP) of the western Pacific subtropical high (WPSH) to investigate the interannual variation of the WPSH in boreal winter. The WRP index based on the theory of gradient wind approximation is particularly suitable for boreal winters. The WRP index comprises two dimensions that depict the zonal and meridional movement of the WPSH, respectively. There is a significant positive correlation between the zonal WRP index and the meridional WRP index. When the WPSH gets stronger, the WRP and the WPSH shift equatorward while advancing westward, and vice versa. The zonal and meridional shifts of the WPSH have distinct impact on the climate anomalies in the western North Pacific and East Asia. The northward shift of the WPSH characterizes a cyclonic-anticyclonic pair over western North Pacific, whereas the eastward shift of the WPSH characterizes a cyclonic anomaly over the subtropical western North Pacific. The anomalies of precipitation and surface air temperature vary accordingly. The meridional shift of the WPSH is closely associated with the El Niño-Southern Oscillation (ENSO) Sea Surface Temperature (SST) anomalies. The WRP index also demonstrates robust predictability in the hindcast data from ENSEMBLES, suggesting its far-reaching potential for climate prediction.

Evaluation and refinement of ERA5-land 2 m atmospheric temperature in GNSS precipitable water vapor

Since the land 2 m air temperature from European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5-land) is vulnerable to interference by regional climate characteristics and local topography, how to objectively and accurately evaluate its accuracy and then establish a refined model is the key to improve its multifaceted and deep-level applications. This study proposed a systematic and complete refinement method for ERA5-land air temperature based on the densely distributed weather stations in Shandong Province in eastern China, including the compensation of elevation matching bias (EMB) caused by ASTER GDEM and the overall refinement using the remove-and-restore model (RRM). Taking the hottest July and the coldest December in this area, surface air temperature, atmospheric weighted mean temperature (Tm) and GNSS precipitable water vapor (PWV) were used to verify the feasibility of this method. The results show that: (1) Whether the temperature is high in July or low in December, the accuracy can be improved by about 42.2 % when the refinement method was used. Especially for high altitude areas (the summit of Mount Tai, 1553 m above sea level), the accuracy can be improved by 74.5 %. (2) In contrast to only the temperature lapse rate (TLR) correction, the average root mean square (RMS) can be reduced by 7.4 % and 60.4 % in July and 7.0 % and 59.5 % in December for the met stations less than 150 m in altitude and the peak station of Mount Tai, respectively, when the new method was used. (3) Compared with the Tm and PWV computed by interpolated temperature without any correction, the accuracy of Tm can be improved by 31.2 % and 34.1 % in July and December for weather station in the altitude of 100 ∼ 350 m after the TLR correction, EMB compensation and RRM refinement successively. Meanwhile, the accuracy of PWV can be improved by 47.8 % in July and 42.0 % in December, respectively, and especially for PWV high-value moments. Besides, after correction by RRM, both Tm and PWV in low altitude areas achieve at least 20 % accuracy improvement. In summary, the systematic and complete refinement method proposed in this study are important to significantly improve the ERA5-land 2 m temperature availability and GNSS PWV accuracy, and the compensation of EMB is mainly effective for high altitude areas.

A Spatial Risk Analysis of Springtime Daily Minimum Surface Air Temperature Values for Vineyard Site Selection: Applications to Pinot noir Grapevines throughout the Willamette Valley American Viticultural Area

A Spatial Risk Analysis of Springtime Daily Minimum Surface Air Temperature Values for Vineyard Site Selection: Applications to Pinot noir Grapevines throughout the Willamette Valley American Viticultural Area

On a relation between shrinking of sea ice coverage and climate warming in the marine Arctic

Arctic amplification (AA) of the climate warming is understood as the excess of the surface air temperature rise in the Arctic over the same process in the non-Arctic latitudes, and it is a fundamental characteristic of the climate during periods of warming. The positive feedback between albedo and shrinking of the sea ice coverage has been identified as the first possible cause of AA. The article presents quantitative estimates of the relationship between the summer decrease and autumn-winter restoration of the ice coverage with the rise of the surface air temperature in the marine Arctic based on data of observations. The mean monthly values of the temperature in the marine Arctic and the mean monthly values of areas covered by the sea ice in the Arctic Ocean and the Arctic seas for the period 1989–2020 were used. It has been found that the observed warming and the decrease of the ice coverage are accompanied by a growth of inter-monthly changes in the coverage and the same in the air temperature. Negative trends in increments of the ice coverage in May–July is indicative of a long-term growth in inter-monthly shrinkage of the ice coverage due to increasing melting, while negative trends in increments of the temperature in the same months is suggestive of a slowdown in the temperature rise from month to month, presumably due to the increasing heat consumption for the snow and ice melting and the water heating. The positive correlation confirms the relation between growing negative inter-monthly differences in the ice coverage and decreasing positive differences in the air temperature during these months. Based on that we determined a sensitivity of the inter-monthly increments of the temperature to the increments of the ice coverage, which was used to estimate the weakening of the positive air temperature trend in May–July over the Arctic Ocean and over the seas of the Northern Sea Route (NSR). Estimating of the relationship between month-to-month changes in temperature and ice coverage in the autumn-winter months meet difficulties due to the strong influence of external heat influx. Only in November and January a slowdown in the air temperature drop from October to November and from December to January had been revealed, with a positive trend in the ice coverage increments.

Unconventional cold vortex as precursor to historic early summer heatwaves in North China 2023

In mid-June to July 2023, North China witnessed extreme heatwaves, marked by intense near-surface warming with an advanced seasonal cycle of local air temperature. An unconventional upper-tropospheric cold vortex in early June, deviating from conventional “heat dome” patterns, preceded the heatwave extremes. The zonal SSTA gradient in Indo-Pacific warm pool initially suppressed Indian summer monsoon convection, which stimulated the cold vortex around North China via a tropical-extratropical teleconnection. This anomaly intensified the air-land thermal contrast, leading to increased sensible heating and reduced soil moisture in situ. The drier soil conditions maintained and further augmented sensible heating, escalating surface air temperature, and culminating in extraordinary heatwaves. The air column was then destabilized to mitigate the upper-level cold vortex. Historical records corroborate the extremity of the air-sea interactions in 2023. The ECMWF real-time subseasonal-to-seasonal (S2S) forecasts successfully capture the air-land feedback in both cold vortex and heatwave stages, albeit with an underestimation of heatwave intensity due to biases in soil moisture anomalies. Consequently, the initial cold vortex condition and air-land-sea interactions yield S2S predictability to the historic 2023 heatwaves in North China.

Anthropogenic Changes in Interannual-to-Decadal Climate Variability in CMIP6 Multiensemble Simulations

Abstract As well as having an impact on the background state of the climate, global warming due to human activities could affect its natural oscillations and internal variability. In this study, we use four initial-condition ensembles from the CMIP6 framework to investigate the potential evolution of internal climate variability under different warming pathways for the twenty-first century. Our results suggest significant changes in natural climate variability and point to two distinct regimes driving these changes. The first is a decrease in internal variability of surface air temperature at high latitudes and all frequencies, associated with a poleward shift and the gradual disappearance of sea ice edges, which we show to be an important component of internal variability. The second is an intensification of the interannual variability of surface air temperature and precipitation at low latitudes, which appears to be associated with El Niño–Southern Oscillation (ENSO). This second regime is particularly alarming because it may contribute to making the climate more unstable and less predictable, with a significant impact on human societies and ecosystems.

Spatial and Temporal Evolution of Seasonal Sea Ice Extent of Hudson Strait, Canada, 1971–2018

The temporal and spatial variation in seasonal sea ice in Hudson Strait is examined using time series and spatial clustering analyses. For the period from 1971 to 2018, a time series of sea ice breakup and freeze-up dates and ice-free season length at twenty-four grid points were generated from sea ice charts derived from satellite and other data. These data were analyzed temporally and spatially. The temporal analyses indicated an unambiguous response to a warming climate with statistically significant earlier breakup dates, later freeze-up dates, and longer ice-free seasons, that were statistically linked to coincident regional surface air temperatures. The rate of change in freeze-up dates and ice-free season length was particularly strong in the early 2000s and less so in the 2010s. There was evidence that breakup date behaviour was not only coincident with regional temperatures but likely with temperature and ice conditions of the previous year. Later freeze-up dates were directly linked to earlier breakup dates using detrended time series. Spatial clustering analysis on the Hudson Strait gridded sea ice data revealed distinctive signatures for Ungava Bay, Frobisher Bay, and for grid points close to the shore and a clear linkage to the underlying circulation of Hudson Strait.

Multiple serial correlations in global air temperature anomaly time series.

Serial correlations within temperature time series serve as indicators of the temporal consistency of climate events. This study delves into the serial correlations embedded in global surface air temperature (SAT) data. Initially, we preprocess the SAT time series to eradicate seasonal patterns and linear trends, resulting in the SAT anomaly time series, which encapsulates the inherent variability of Earth's climate system. Employing diverse statistical techniques, we identify three distinct types of serial correlations: short-term, long-term, and nonlinear. To identify short-term correlations, we utilize the first-order autoregressive model, AR(1), revealing a global pattern that can be partially attributed to atmospheric Rossby waves in extratropical regions and the Eastern Pacific warm pool. For long-term correlations, we adopt the standard detrended fluctuation analysis, finding that the global pattern aligns with long-term climate variability, such as the El Niño-Southern Oscillation (ENSO) over the Eastern Pacific. Furthermore, we apply the horizontal visibility graph (HVG) algorithm to transform the SAT anomaly time series into complex networks. The topological parameters of these networks aptly capture the long-term correlations present in the data. Additionally, we introduce a novel topological parameter, Δσ, to detect nonlinear correlations. The statistical significance of this parameter is rigorously tested using the Monte Carlo method, simulating fractional Brownian motion and fractional Gaussian noise processes with a predefined DFA exponent to estimate confidence intervals. In conclusion, serial correlations are universal in global SAT time series and the presence of these serial correlations should be considered carefully in climate sciences.

Cumulative Changes in Minimum Snow/Ice Extent over Canada and Northern USA for 2000–2023

The Minimum Snow/Ice (MSI) extent is an important climate and environmental indicator related to surface hydrology and freshwater resources. Variations in the MSI extent over the four first-order regions (1-4) included in the Randolph Glacier Inventory (RGI) for Canada and the Northern USA were analyzed in this study using an improved land-water mask to better discriminate landfast ice in the high Arctic coastal zone. The analysis utilized warm season snow/ice probability maps derived from MODIS clear-sky composite imagery at a 250-m spatial resolution. Results showed statistically significant declining trends in the minimum snow/ice extent with the average value of the slope for the entire area (-1,177.7 ± 362.8) km2/yr. The variations in MSI extent are well correlated with surface air temperature. The correlation coefficients are statistically significant, ranging from −0.79 to −0.82. The detailed analysis revealed a reduction of the area with snow/ice probability 100% over the RGI glaciated zones, while areas with lower probability values were increasing in size, which may be interpreted as an indication of general glacier shrinkage over the study area.

Contributions of Soil Moisture and Vegetation on Surface-Air Temperature Difference during the Rapid Warming Period

The difference (DIF) between land surface temperature (Ts) and near surface air temperature (Ta) is the key indicator of the energy budget of the land surface, which has a more complex process than the individual Ts or Ta. However, the spatiotemporal variations and influencing factors of DIF remain incomplete. The contribution of vegetation and soil moisture (SM) as key driving factors to DIF is not yet clear. Here, we analyzed the spatiotemporal variation patterns of DIF in China from 2011 to 2023 using in situ Ts and Ta data. A convergent cross-mapping method was employed to explore the causal relationship between SM, NDVI and DIF, and subsequently calculated the contribution of NDVI and SM variations to DIF under different climatic backgrounds. The results indicate that during the study period, DIF values were all above 0 °C and showed a significant increasing trend with a national mean slope of 0.02 °C/a. In general, vegetation and SM have a driving effect on DIF, with vegetation contributing more to DIF (0.11) than SM (0.08) under different surface properties. The background values of SM and temperature have a significant effect on the spatial and temporal distribution of DIF, as well as the correlation of vegetation and soil moisture to DIF. The study outcomes contribute to a better understanding of the coupling relationship between the land surface and atmosphere, which are also crucial for addressing climate change and ecological environmental management.

Co‐Benefits of Mitigating Aerosol Pollution to Future Solar and Wind Energy in China Toward Carbon Neutrality

AbstractThe climate commitment to achieving carbon neutrality before 2060 in China has been announced recently. In the context of pursuing carbon neutrality, sharing similar sources as greenhouse gases, aerosol particle and precursor emissions are projected to substantially decrease in China, which can potentially have a great impact on climate. Here, we investigate the effects of future aerosol reductions, because of achieving carbon neutrality, on solar and wind energy in China by using an earth system model. We show that significant reductions in aerosol emissions, particularly in eastern China, lead to increases in the surface downwelling shortwave radiation, surface air temperature and wind speed, which can further enhance the potential of solar and wind energy production. The findings underline that the pursuit of carbon neutrality can yield co‐benefits of not only mitigating climate change and air pollution but also fortifying the stability of renewable energy sources.

Performance of CMIP5 and CMIP6 models in reproducing the Interdecadal Pacific Oscillation and its global impacts

AbstractThis study assessed the capability of the historical simulations of phase 5 and 6 of the Coupled Model Intercomparison Project (CMIP5/6) in reproducing the temporal and spatial characteristics of the Interdecadal Pacific Oscillation (IPO) and its impact on global surface air temperature (SAT), surface equivalent potential temperature (Thetae_sfc) and precipitation. The IPO index time series simulated by CMIP5/6 models deviated from observations and struggled to capture the phase evolution characteristics of the IPO. Nevertheless, CMIP5/6 models successfully captured the horseshoe‐shaped sea surface temperature anomaly in the Pacific. Additionally, the CMIP5/6 models were able to simulate the IPO's 10–30‐year period. Notably, the simulated IPO index exhibited a statistically significant upward trend, which was absent in observations. Additionally, the IPO‐related global land SAT, Thetae_sfc and precipitation simulated by CMIP5/6 models performed differently in boreal winter and boreal summer. Furthermore, the IPO‐related global land SAT performed better in CMIP5/6 models during boreal winter than that in boreal summer. In CMIP6 models, it improved during both boreal winter and summer compared to CMIP5 models. In terms of the IPO‐related global land Thetae_sfc, CMIP5/6 models also performed better during boreal winter than in boreal summer. However, CMIP5 models outperformed CMIP6 models during the boreal summer. In terms of the IPO‐related global land precipitation, CMIP5/6 models performed better during boreal summer compared to boreal winter. Moreover, the IPO‐related global land precipitation in CMIP6 models improved significantly in boreal winter, but almost the same in boreal summer, compared to CMIP5 models. Further studies showed that the enhancements in simulating IPO's spatial pattern did not correspond to improvements in the model's ability to simulate IPO's global teleconnections.

Can climate change signals be detected from the terrestrial water storage at daily timescale?

The global terrestrial water storage (TWS), the most accessible component in the hydrological cycle, is a general indicator of freshwater availability on Earth. The global TWS trend caused by climate change is harder to detect than global mean temperature due to the highly uneven hydrological responses across the globe, the brevity of global freshwater observations, and large noises of internal climate variability. To overcome the climate noise and small sample size of observations, we leverage the vast amount of observed and simulated meteorological fields at daily scales to project global TWS through its fingerprints in weather patterns. The novel method identifies the relationship between annual global mean TWS and daily surface air temperature and humidity fields using multi-model hydrological simulations. We found that globally, approximately 50% of days for most years since 2016 have climate change signals emerged above the noise of internal variability. Climate change signals in global mean TWS have been consistently increasing over the last few decades, and in the future, are expected to emerge from the natural climate variability. Our research indicates the urgency to limit carbon emission to not only avoid risks associated with warming but also sustain water security in the future.

ВЕРИФИКАЦИЯ СЕЗОННЫХ АНСАМБЛЕВЫХ ПРОГНОЗОВ НА БАЗЕ МОДЕЛИ ЗЕМНОЙ СИСТЕМЫ INM-CM5

The issues related to the verification of ensemble seasonal forecasts obtained using a new technology implemented on the basis of the INM-CM5 Earth system model are considered. The forecast objects are global gridded (2.5° × 2.5°) fields of ensemble mean anomalies and probabilities of three gradations of the anomalies (below normal, normal, above normal) for each of the five characteristics: 500 hPa geopotential height (H500), mean sea level pressure (SLP), 850 hPa air temperature (T850), surface air temperature (T2m), and precipitation (Prec) for six periods of time averaging (at monthly intervals for the 1st, 2nd, 3rd, 4th months and at two seasonal intervals: season 1 (the 1st-3rd months) and season 2 (the 2nd-4th months)). It is noted that forecast skill scores vary greatly depending on a region, a season, and a meteorological parameter. The best results for all parameters were obtained for the tropics, where the main sources of long-term predictability of the atmosphere are located. In extratropical latitudes, the quality of forecasts, especially at the monthly intervals, decreases and approaches the level of climate forecasts. The skill of precipitation forecasts is significantly inferior to the quality of forecasts of other meteorological parameters. It is noted that the success in ensemble seasonal probabilistic and deterministic forecasting of the main meteorological elements with the INM-CM5 model, both on a global scale and in individual regions, is comparable with the skill scores of similar forecasts of the foreign meteorological centers participating in the LC MME-WMO project. The introduction of the new climate model into the scientific and operational practice of the Hydrometcenter of Russia/North Eurasia Climate Centre will contribute to improving the quality of monthly and seasonal forecasts and developing specialized climate services.

The Subseasonal Feedback of Extreme Anomalous Tibetan Plateau Snow Cover Events on the Atmosphere

Abstract The Tibetan Plateau snow cover exhibits notable subseasonal variability and plays a crucial role in influencing the atmosphere. This study employs numerical experiments to investigate the atmospheric feedback resulting from extreme anomalous snow cover events on the Tibetan Plateau, with a focus on both local and nonlocal atmospheric temperatures. The findings reveal that diabatic heating, directly induced by these events, leads to a local surface energy cooling response over the Tibetan Plateau, contributing to a reduction in local temperatures. This cooling effect amplifies local atmospheric temperature anomalies associated with extreme anomalous Tibetan Plateau snow cover events, constituting approximately 50% of the total final local surface air temperature anomalies. Furthermore, the Tibetan Plateau snow cover, through adiabatic processes, exerts a nonlocal influence on atmospheric temperature and circulation. The atmospheric temperature responses downstream of the Tibetan Plateau vary at different heights and regions, featuring both cold and warm anomaly responses. These variations depend on the relative contributions of horizontal advection and vertical advection in adiabatic heating. Significance Statement Snow cover is influenced by the atmosphere, and in turn, it affects the atmosphere. This study examines the feedback of extreme anomalous Tibetan Plateau snow cover events on the atmosphere at the subseasonal time scale, with a focus on atmospheric temperature. Our findings indicate that Tibetan Plateau snow cover has both local and nonlocal feedback effects on the atmosphere. In particular, extreme anomalous Tibetan Plateau snow cover amplifies local surface air temperature anomalies. The feedback contributes to approximately 50% of the total final local surface air temperature anomalies.

Testing the Sensitivity of a WRF-Based Great Lakes Regional Climate Model to Cumulus Parameterization and Spectral Nudging

Abstract The Weather Research and Forecasting (WRF) Model is used to dynamically downscale ERA-Interim global reanalysis data to test its performance as a regional climate model (RCM) for the Great Lakes region (GLR). Four cumulus parameterizations and three spectral nudging techniques applied to moisture are evaluated based on 2-m temperature and precipitation accumulation in the Great Lakes drainage basin (GLDB). Results are compared to a control simulation without spectral nudging, and additional analysis is presented showing the contribution of each nudged variable to temperature, moisture, and precipitation. All but one of the RCM test simulations have a dry precipitation bias in the warm months, and the only simulation with a wet bias also has the least precipitation error. It is found that the inclusion of spectral nudging of temperature dramatically improves a cold-season cold bias, and while the nudging of moisture improves simulated annual and diurnal temperature ranges, its impact on precipitation is complicated. Significance Statement Global climate models are vital to understanding our changing climate. While many include a coarse representation of the Great Lakes, they lack the resolution to represent effects like lake effect precipitation, lake breeze, and surface air temperature modification. Therefore, using a regional climate model to downscale global data is imperative to correctly simulate the land–lake–atmosphere feedbacks that contribute to regional climate. Modeling precipitation is particularly important because it plays a direct role in the Great Lakes’ water cycle. The purpose of this study is to identify the configuration of the Weather Research and Forecasting Model that best simulates precipitation and temperature in the Great Lakes region by testing cumulus parameterizations and methods of nudging the regional model toward the global model.

Climate network analysis of extreme events: Tropical cyclones.

Climate networks based on surface air temperature data are analyzed to identify distinct signatures of tropical cyclones, which appear in the Indian Ocean. These networks, which are percolating networks, show an abrupt phase transition in the order parameter and the susceptibility during cyclonic events. The behavior seen is compared for the months October-November 2016, when three successive cyclones, viz., cyclone Kyant, cyclone Nada, and cyclone Vardah, were seen, and compared with a year where a single cyclone, cyclone Ockhi, was seen in December 2017. All these cyclones were seen in the Bay of Bengal. The microtransitions, i.e., the locations of jumps in the order parameter, for these two cases show distinct patterns. The signatures of the cyclones can be seen in other quantities like the node degrees and their geographic distributions and other network characterizers. We also compare these with a cyclone, cyclone Ashoba (2015), seen in the Arabian Sea where cyclones are rarer. The networks also show the signatures of precursor behavior, which has implications for further analysis.

Comparative Study on the Accuracy of Surface Air Temperature Prediction based on selection of land use and initial meteorological data

Comparative Study on the Accuracy of Surface Air Temperature Prediction based on selection of land use and initial meteorological data

Reconstructed Late Summer Maximum Temperatures for the Southeastern United States From Tree‐Ring Blue Intensity

AbstractOver recent decades, the southeastern United States (Southeast) has become increasingly well represented by the terrestrial climate proxy record. However, while the paleo proxy records capture the region's hydroclimatic history over the last several centuries, the understanding of near surface air temperature variability is confined to the comparatively shorter observational period (1895‐present). Here, we detail the application of blue intensity (BI) methods on a network of tree‐ring collections and examine their utility for producing robust paleotemperature estimates. Results indicate that maximum latewood BI (LWBI) chronologies exhibit positive and temporally stable correlations (r = 0.28–0.54, p < 0.01) with summer maximum temperatures. As such, we use a network of LWBI chronologies to reconstruct August‐September average maximum temperatures for the Southeast spanning the period 1760–2010 CE. Our work demonstrates the utility of applying novel dendrochronological techniques to improve the understanding of the multi‐centennial temperature history of the Southeast.

Modulation of QBO on the relationship between stratospheric polar vortex and surface air temperature over Eurasia during early winter

Modulation of QBO on the relationship between stratospheric polar vortex and surface air temperature over Eurasia during early winter