Changes In Near-surface Temperature Research Articles

Dynamic analysis of landscape drivers in the thermal environment of Guanzhong plain urban agglomeration

Climate change caused by rapid urbanization in the Guanzhong region of China is becoming an increasingly significant problem. Previous empirical studies have confirmed that landscape patterns inextricably linked with the thermal environment, but static results based on a single temporal cross section of image data provide only a partial understanding. In this paper, we constructed a dynamic framework using Weather Research and Forecasting Model (WRF) for temperature simulation and Geodetector to study the landscape factors and their interactions that influence near-surface temperature (NST) changes in the Guanzhong Plain Urban Agglomeration (GPUA) between 2000 and 2020. Results showed that the GPUA average NST increased by 0.012 °C and 0.053 °C in January and July from 2000 to 2020, respectively. In terms of the dynamic correlation between landscape patterns and NST, cropland (CPL) was negative, urban land (UBL) was positive, and the remainder of the landscapes differed in winter and summer. Furthermore, results from the Geodetector showed that UBL embodied a stronger influence in summer than during winter months. This finding helps to explain why the average NST increase is higher in summer than during winter. The Dynamic Q values (DQ) of the area-based landscape metrics were generally larger than those of other spatial configuration metrics, and the interaction results showed that the landscape metrics of various land-cover classifications were enhanced, indicating that the superposition effect among landscape metrics needs to be taken into account in landscape planning in addition to area factors. The study of the relationship between landscape patterns and thermal environment considering dynamic perspective using WRF offers an important theoretical reference allied with practical guidance for understanding and adapting to forthcoming change in our climate through which we can help drive sustainable development decisions of the GPUA.

Modeled temperature, mortality impact and external benefits of cool roofs and rooftop photovoltaics in London

Population exposure to high temperatures poses health risks and increases mortality. ‘Cool roofs’ (high-albedo roofs) and rooftop photovoltaics (RPV) may reduce temperatures in urban areas. Here, using advanced urban climate modeling, we model impacts of these measures on air temperature and heat-related mortality in London during the record-breaking hot summer of 2018. We estimate changes in mean near-surface air temperature of −0.3 °C in the RPV scenario and −0.8 °C in the cool roof scenario. We find that the heat-related mortality in this period (estimated 655–920) could have been reduced by 96 (12%) by RPV, or 249 (32%) by cool roofs, in scenarios where all roofs have these measures. Monetized using value of statistical life, we estimate benefits for RPV and cool roofs of £237 M and £615 M, respectively. We estimate that up to 20 TWh of electrical energy would be generated in the full RPV scenario. We show that, for conditions such as in London June–August 2018, RPV or cool roofs may reduce near-surface air temperatures and associated heat-related mortality, with cool roofs having a larger effect.

Near-surface ocean temperature and air-sea heat flux observed by a buoy array during summer to autumn in year 2014 in the northern South China Sea

The observational data of the air-sea interface and upper ocean elements from a buoy array deployed in the northern South China Sea from June to November 2014 is analyzed. The COARE 3.0 algorithm was adopted to examine the variability of air-sea heat fluxes and their contributions to near-surface ocean temperature changes. During the observation period, air-sea heat exchange in the northern South China Sea is primarily determined by solar shortwave radiation and latent heat flux, with net heat flux mostly negative, indicating that the ocean predominantly loses heat. In autumn, net heat flux loss significantly increased by an average of 34.4 W/m2, primarily due to enhanced latent heat loss driven by stronger winter monsoon. During typhoon period, net air-sea heat flux was significantly suppressed, mainly due to reduced solar shortwave radiation, with minimal contribution from sensible heat flux. Near-surface ocean temperature exhibited a seasonal cooling trend, averaging 29.7°C in summer and 28.4°C in autumn. The maximum cooling during typhoon reached 3.1°C, with minimal contribution from air-sea heat flux. During the monsoon transition period, weaker winds led to a slight increase in near-surface ocean temperature, with air-sea heat flux contributing 70.2% and 56.3% to this process. During winter monsoon, more uniform water column mixing resulted in a gradual decrease in near-surface ocean temperature, with air-sea heat flux contributing over 60% to this process.

Impacts of projected urban growth on simulated near-surface temperature in Mexico City Metropolitan Area: Implications for urban vulnerability

Urbanization impacts the surface temperature fields increasing the vulnerability of urban residents to heat exposure. Identifying vulnerable urban populations to extreme heat exposure is crucial to develop mitigation and adaptation strategies towards sustainability. We used an urban growth model (SLEUTH) to simulate emerging urban areas in Mexico City Metropolitan Area (MCMA) under a hypothetical land-use policy scenario projected to 2060 in which no restrictions were posed to urban growth. SLEUTH outputs were used in the numerical model Weather Research and Forecasting (WRF) to quantify expected changes in near-surface temperature within the MCMA. We calculated and mapped heat exposure as differences in average (Tmean), maximum (Tmax) and minimum (Tmin) temperatures over the diurnal cycle between future and current land cover conditions. Population vulnerability to projected increases in heat exposure was determined using a set of socioeconomic indicators. SLEUTH simulations showed an urban area expansion of nearly 4,790 km2 by 2060. Overall, changes in Tmin were greater than changes observed for Tmax and Tmean. Tmean, Tmax and Tmin increases up to 0.6°C, 1.3°C and 2.6°C, respectively, were recorded for the MCMA with greatest temperature changes observed in the State of Mexico. Results suggested the presence of socioeconomic disparities in the projected spatial exposure of urban-induced heat in MCMA. We argue that our results could be used to inform and guide locally tailored actions aimed at reducing exposure and increasing population´s capacities to cope and adapt to future threats.

Modeling the influence of climate on groundwater flow and heat regime in Brandenburg (Germany)

This study investigates the decades-long evolution of groundwater dynamics and thermal field in the North German Basin beneath Brandenburg (NE Germany) by coupling a distributed hydrologic model with a 3D groundwater model. We found that hydraulic gradients, acting as the main driver of the groundwater flow in the studied basin, are not exclusively influenced by present-day topographic gradients. Instead, structural dip and stratification of rock units and the presence of permeability contrasts and anisotropy are important co-players affecting the flow in deep seated saline aquifers at depths >500 m. In contrast, recharge variability and anthropogenic activities contribute to groundwater dynamics in the shallow (<500 m) freshwater Quaternary aquifers. Recharge fluxes, as derived from the hydrologic model and assigned to the parametrized regional groundwater model, reproduce magnitudes of recorded seasonal groundwater level changes. Nonetheless, observed instances of inter-annual fluctuations and a gradual decline of groundwater levels highlight the need to consider damping of the recharge signal and additional sinks, like pumping, in the model, in order to reconcile long-term groundwater level trends. Seasonal changes in near-surface groundwater temperature and the continuous warming due to conductive heat exchange with the atmosphere are locally enhanced by forced advection, especially in areas of high hydraulic gradients. The main factors controlling the depth of temperature disturbance include the magnitude of surface temperature variations, the subsurface permeability field, and the rate of recharge. Our results demonstrate the maximum depth extent and the response times of the groundwater system subjected to non-linear interactions between local geological variability and climate conditions.

Observed Anomaly of Temperature and Mixed Layer Depth Associated with the Madden Julian Oscillation (MJO) Active Phase in the Banda Sea, Indonesia

The eastward propagation of atmospheric waves along the equatorial band from the central equatorial Indian Ocean to the western Pacific Ocean passing through the Indonesian Maritime Continent (IMC), known as the intraseasonal Madden Julian Oscillation (MJO) event, plays an important role on modulating both atmospheric and upper ocean dynamics along its path. This study aims to investigate the MJO active phase dynamics and its impact on changes in near-surface seawater temperature and mixed layer depth (MLD) anomaly in the Banda Sea Indonesia, using multi-datasets of atmospheric reanalysis, satellites derived sea surface temperature (SST), and Argo float between 2017 - 2018. This study revealed that the MJO waves propagate eastward along the southern equator-line over the IMC and pass through the Banda Sea, associated with significant decreased on Outgoing Longwave Radiation (OLR) and increased in zonal wind speed at 850hPa. The study result shows anomalous increased on surface wind speed and SST cooling during of MJO active phase. The amplitude peaks of filtered ocean-atmosphere variables range between 30 – 60 days. Argo float datasets in the Banda Sea for the first time captured upper ocean responses to the arrival of the MJO active phase, as characterized by a negative temperature anomaly of ~0.3°C in the surface mixed layer, large temperature anomaly of ~0.8°C in the thermocline layer and the deepening of the MLD of ~25 m. Hence, the MJO active phase impacts significantly on surface and vertical temperature cooling and regulate upper ocean mixing intensity in the Banda Sea.

Characteristics of boundary layer height and its influencing factors in global monsoon regions

Monsoon precipitation changes has led to substantial changes in the spatial pattern of land surface energy in most monsoon regions of the world, which subsequently affect the structure and development of the atmospheric boundary layer (ABL) over land. Based on the ERA-Interim reanalysis data (1979–2018), the present study analyzes the long-term variability characteristics of Boundary layer height (BLH) and its influencing factors in the global land monsoon region. The results show that the long-term annual mean BLH averaged over all the monsoon regions across the globe is approximately 653.2 m, but there exist great differences between various regions. The highest BLH occurs in the Australian (AUS) region, followed by that in South Africa (SAF), North Africa (NAF), North America (NAM) and South America (SAM) regions, and the lowest BLH occurs in the Asian (ASN) monsoon region. The differences in BLH between various regions reflect significant regional differences in surface energy distribution. A larger sensible heat flux SHF usually corresponds to a higher BLH, while NAM with the largest latent heat flux LHF corresponds to not the lowest BLH, which is related to its smaller cloud radiation cooling. In the past 40 years, the spatial variation of BLH in ASN region is the most special among all monsoon regions, which is mainly manifested in the increasing trend in the east (east of 100°E) and the decreasing trend in the west (west of 100°E), partly due to the distribution of more precipitation in the east and less precipitation in the west in recent decades. The differences in BLH distribution in different monsoonal regions are mainly caused by SHF. Wind shearSand vertical sensible heat advectionHmv, as the main driving sources of ABL development, play a non-negligible role in the variances of BLH structure in different monsoon areas, although their contributions are much smaller than that of SHF.The role of S in promoting or inhibiting the development of ABL is not only related to S size, but also closely related to the atmospheric turbulence environment. Additionally, each monsoon region's Hmv influence on BLH is the most complex, and changes in near-surface temperature and annual precipitation can only partially explain the observed occurrences. The complicated process must also take into account the significance of local vertical motion brought on by weather variations and surface irregularities in various monsoon regions.

A Python library for computing individual and merged non-CO2 algorithmic climate change functions: CLIMaCCF V1.0

Abstract. Aviation aims to reduce its climate effect by adopting trajectories that avoid regions of the atmosphere where aviation emissions have a large impact. To that end, prototype algorithmic climate change functions (aCCFs) can be used, which provide spatially and temporally resolved information on aviation's climate effect in terms of future near-surface temperature change. These aCCFs can be calculated with meteorological input data obtained from, e.g., numerical weather prediction models. We present here the open-source Python library called CLIMaCCF, an easy-to-use and flexible tool which efficiently calculates both the individual aCCFs (i.e., aCCF of water vapor, nitrogen oxide (NOx)-induced ozone production and methane depletion, and contrail cirrus) and the merged non-CO2 aCCFs that combine all these individual contributions. To construct merged aCCFs all individual aCCFs are converted to the same physical unit. This unit conversion needs the technical specification of aircraft and engine parameters, i.e., NOx emission indices and flown distance per kilogram of burned fuel. These aircraft- and engine-specific values are provided within CLIMaCCF version V1.0 for a set of aggregated aircraft and engine classes (i.e., regional, single-aisle, wide-body). Moreover, CLIMaCCF allows the user to choose from a range of physical climate metrics (i.e., average temperature response for pulse or future scenario emissions over the time horizons of 20, 50, or 100 years). Finally, we demonstrate the abilities of CLIMaCCF through a series of example applications.

The Sensitivity of Green-Up Dates to Different Temperature Parameters in the Mongolian Plateau Grasslands

The rise in global average surface temperature has promoted the advancement of spring vegetation phenology. However, the response of spring vegetation phenology to different temperature parameters varies. The Mongolian Plateau, one of the largest grasslands in the world, has green-up dates (GUDs) with unclear sensitivity to different temperature parameters. To address this issue, we investigated the responses of GUDs to different temperature parameters in the Mongolian Plateau grasslands. The results show that GUDs responded significantly differently to changes in near-surface temperature (TMP), near-surface temperature maximum (TMX), near-surface temperature minimum (TMN), and diurnal temperature range (DTR). GUDs advanced as TMP, TMX, and TMN increased, with TMN having a more significant effect, whereas increases in DTR inhibited the advancement of GUDs. GUDs were more sensitive to TMX and TMN than to TMP. The sensitivity of GUDs to DTR showed an increasing trend from 1982 to 2015 and showed this parameter’s great importance to GUDs. Our results also show that the spatial and temporal distributions of temperature sensitivity are only related to temperature conditions in climatic zones instead of whether they are arid.

Alaska Terrestrial and Marine Climate Trends, 1957–2021

Abstract Some of the largest climatic changes in the Arctic have been observed in Alaska and the surrounding marginal seas. Near-surface air temperature (T2m), precipitation (P), snowfall, and sea ice changes have been previously documented, often in disparate studies. Here, we provide an updated, long-term trend analysis (1957–2021; n = 65 years) of such parameters in ERA5, NOAA U.S. Climate Gridded Dataset (NClimGrid), NOAA National Centers for Environmental Information (NCEI) Alaska climate division, and composite sea ice products preceding the upcoming Fifth National Climate Assessment (NCA5) and other near-future climate reports. In the past half century, annual T2m has broadly increased across Alaska, and during winter, spring, and autumn on the North Slope and North Panhandle (T2m > 0.50°C decade−1). Precipitation has also increased across climate divisions and appears strongly interrelated with temperature–sea ice feedbacks on the North Slope, specifically with increased (decreased) open water (sea ice extent). Snowfall equivalent (SFE) has decreased in autumn and spring, perhaps aligned with a regime transition of snow to rain, while winter SFE has broadly increased across the state. Sea ice decline and melt-season lengthening also have a pronounced signal around Alaska, with the largest trends in these parameters found in the Beaufort Sea. Alaska’s climatic changes are also placed in context against regional and contiguous U.S. air temperature trends and show ∼50% greater warming in Alaska relative to the lower-48 states. Alaska T2m increases also exceed those of any contiguous U.S. subregion, positioning Alaska at the forefront of U.S. climate warming. Significance Statement This study produces an updated, long-term trend analysis (1957–2021) of key Alaska climate parameters, including air temperature, precipitation (including snowfall equivalent), and sea ice, to inform upcoming climate assessment reports, including the Fifth National Climate Assessment (NCA5) scheduled for publication in 2023. Key findings include widespread annual and seasonal warming with increased precipitation across much of the state. Winter snowfall has broadly increased, but spring and autumn snowfalls have decreased as rainfall increased. Autumn warming and precipitation increases over the North Slope, in particular, appear related to decreased sea ice coverage in the Beaufort Sea and Chukchi Seas. These trends may result from interrelated processes that accelerate Alaska climate changes relative to those of the contiguous United States.

Biogeophysical Effects of Land-Use and Land-Cover Changes in South Asia: An Analysis of CMIP6 Models

The identification of the biogeophysical effects due to land-use, land-cover, and land- management changes (LULCC) is yet to be clearly understood. A range of factors, such as the inclusion of an interactive ocean model component, representation of land management, transient LULCC, and accountability for atmospheric feedback, potentially shifts how models may detect the impacts of the land surface on the climate system. Previous studies on the biogeophysical effects of LULCC in South Asia have either neglected one of those factors or are single model results. Therefore, we analyzed the outputs from 11 models, participants of the Coupled Model Intercomparison Project in its Sixth Phase (CMIP6), which derived from experiments with and without LULCC and compared the two simulations with respect to changes in near-surface temperature and total precipitation means. The CMIP6 simulations, to a certain extent, accounted for the elements previously overlooked. We examined the grid cells that robustly indicated a climatic impact from LULCC. Additionally, we investigated the atmospheric feedback and the dominant fluxes with their associated land surface variables involved in the changes in temperature and precipitation. Our results indicated that the biogeophysical effects from LULCC favored surface net cooling and surface net drying over the robust areas at all seasons. The surface net cooling was strongly influenced by the decrease in available energy and the increase in latent heat and total evapotranspiration. Surface net drying was highly promoted by local hydrological processes, especially in areas outside the monsoon core. The study also revealed that non-local sources might influence precipitation in some parts of South Asia, although this was inconclusive. Our research presented similar results to previous studies but with different magnitudes, which highlighted the added value of CMIP6-GCMs simulations but also their pitfalls.

The Possible Influence of Cosmic Rays on the Planetary Albedo of the Earth

The monthly average values of the planetary albedo at the top-of-atmosphere (TOA) and the averagealbedo values of the hemispheres were obtained based on the results of measurements for the flux of shortwavereflected solar radiation, carried out onboard the Meteor-M No. 2 satellite in 2014–2019. The globallyaveraged albedo shows an increase over time, as evidenced by the presence of a statistically significant lineartrend. We show that this trend is not associated with a change in the average near-surface temperature of theplanet. It is possible that the increase in albedo is explained by an increase in cloudiness caused by an increasein the flux of galactic cosmic rays during the decline of the solar activity cycle

Changes in near-surface permafrost temperature and active layer thickness in Northeast China in 1961–2020 based on GIPL model

Characteristics of active layer processes depend on the conditions of the ground surface, coupled water-heat balance, soil hydrology, and soil properties under frozen and thawed states at shallow depths. Mean annual temperature at the bottom of the active layer (MATBAL) and active layer thickness (ALT) are important metrics in studying the features of active layer processes and the thermal stability of permafrost. Affected by the changing climate, permafrost regions in Northeast China have undergone remarkable changes in the past 30 years, many of which are still ongoing. In Northeast China, however, model studies on examining hydrothermal dynamics and on changes in frozen ground have faced serious challenges, including a complex ecological environment and rugged terrains. In this study, the Geophysical Institute Permafrost Lab (GIPL) model was used to map temporal and spatial variations in MATBAL and ALT in Northeast China, where discontinuous, island, and sporadic permafrost coexists with seasonal frost. Using the parameters of ground surface temperature (GST) and soil properties as input, we applied the GIPL model to analyze the distributive characteristics of near-surface permafrost in Northeast China. The results indicate a sharply shrinking permafrost area from 5.49 × 105 to 2.29 × 105 km2 but rapidly rising rates of 0.17–0.83 °C/decade in MATBAL in Northeast China during the period from 1961 to 2020. Under a persistently warming climate, accelerating permafrost degradation will inevitably affect the hydrological environment, boreal ecosystem, soil biology, and engineering infrastructure. This work fills a gap in the distribution of MATBAL and ALT in Northeast China.

Passive ground-based remote sensing of radiation fog

Abstract. Accurate boundary layer temperature and humidity profiles are crucial for successful forecasting of fog, and accurate retrievals of liquid water path are important for understanding the climatological significance of fog. Passive ground-based remote sensing systems such as microwave radiometers (MWRs) and infrared spectrometers like the Atmospheric Emitted Radiance Interferometer (AERI), which measures spectrally resolved infrared radiation (3.3 to 19.2 µm), can retrieve both thermodynamic profiles and liquid water path. Both instruments are capable of long-term unattended operation and have the potential to support operational forecasting. Here we compare physical retrievals of boundary layer thermodynamic profiles and liquid water path during 12 cases of thin (LWP<40 g m−2) supercooled radiation fog from an MWR and an AERI collocated in central Greenland. We compare both sets of retrievals to in-situ measurements from radiosondes and surface-based temperature and humidity sensors. The retrievals based on AERI observations accurately capture shallow surface-based temperature inversions (0–10 m a.g.l.) with lapse rates of up to −1.2 ∘C m−1, whereas the strength of the surface-based temperature inversions retrieved from MWR observations alone are uncorrelated with in-situ measurements, highlighting the importance of constraining MWR thermodynamic profile retrievals with accurate surface meteorological data. The retrievals based on AERI observations detect fog onset (defined by a threshold in liquid water path) earlier than those based on MWR observations by 25 to 185 min. We propose that, due to the high sensitivity of the AERI instrument to near-surface temperature and small changes in liquid water path, the AERI (or an equivalent infrared spectrometer) could be a useful instrument for improving fog monitoring and nowcasting, particularly for cases of thin radiation fog under otherwise clear skies, which can have important radiative impacts at the surface.

A Model between Near-Surface Air Temperature Change and Dynamic Influencing Factors in the Eastern Tibetan Plateau, China

Climate change, characterized by global warming, is profoundly affecting the global environment, politics, economy, and social security. Finding the main causes of climate change and determining their quantitative contributions are key points to making climate decisions on responses to climate change. The Tibetan Plateau (TP) is sensitive to global climate change. Taking the 100 km buffer zones of 45 meteorological stations in the eastern TP as research objects, we conducted an experimental study on temperature change and its influencing factors. Using the least squares multivariate statistical analysis method, a model between the annual and seasonal standardized temperature change and its dynamic influencing factors in the past 20 years was established. The results showed that, in the eastern TP, temperature change was affected by different factors in different periods. Vegetation cover and snow cover were the most correlated factors to temperature change. The influence of carbon dioxide, vegetation cover, and water cover was subject to seasonal changes. Urban cover and bare land cover did not pass the t-test. This research not only provides a theoretical basis for the analysis of temperature change over the TP, but also points out the direction for the analysis of temperature change causes in three polar regions.

Inter-model variability of the CMIP5 future projection of Baiu, Meiyu, and Changma precipitation

Many studies have suggested that mean precipitation associated with the East Asian Summer Monsoon (EASM) will be increased by the ongoing global warming, but its quantitative projection by climate models has large variability, with some models suggesting even decreases. We investigate the inter-model variability of projected centennial changes of the EASM separately for Baiu over Japan, Meiyu over eastern China, and Changma over Korea by using monthly-mean model outputs provided by the Coupled Model Intercomparison Project Phase 5 (CMIP5) project. Results with the Representative Concentration Pathway 4.5 and 8.5 scenarios (RCP4.5 and RCP8.5) are consolidated by normalizing with the global-mean near-surface air temperature changes. For all the three EASM land regions, inter-model differences in the mean precipitation changes are positively correlated with the southerly moisture flux changes to the south of the regions. The correlation is highest in June among the June-to-August months, whose reason may be because precipitation in early summer relies on large-scale southerly moisture transport. These changes are localized and nearly independent among the three regions where Baiu, Meiyu and Changma occur. The low-level southerly change to the south of Japan, which affects the Baiu precipitation change, is positively correlated with upper-tropospheric meridional wind to its north; it further exhibits a stationary Rossby-wave feature associated with the Silk-Road teleconnection. This study suggests that future changes in the EASM mean precipitation depend on circulation changes and more-or-less localized.

Diagnosing Instantaneous Forcing and Feedbacks of Downwelling Longwave Radiation at the Surface: A Simple Methodology and Its Application to CMIP5 Models

Abstract Climate models predict large increases in downwelling longwave radiation (DLR) at Earth’s surface as atmospheric CO2 concentrations increase. Here we introduce a novel methodology that allows these increases to be decomposed into direct radiative forcing due to enhanced CO2 and feedbacks due to subsequent changes in atmospheric properties. For the first time, we develop explicit analytic expressions for the radiative forcing and feedbacks, which are calculable from time-mean fields of near-surface air temperature, specific humidity, pressure, total column water vapor, and total cloud fraction. Our methodology captures 90%–98% of the variance in changes in clear-sky and all-sky DLR in five CMIP5 models, with a typical error of less than 10%. The longwave feedbacks are decomposed into contributions from changes in temperature, specific humidity, water vapor height scale, and cloud fraction. We show that changes in specific humidity and height scale are closely linked to changes in near-surface air temperature and therefore, in the global average, that 90% of the increase in all-sky DLR may be attributed to a feedback from increasing near-surface air temperature. Mean-state clouds play a major role in changes in DLR by masking the clear-sky longwave and enhancing the temperature feedback via increased blackbody radiation. The impact of changes in cloud cover (the cloud feedback) on the DLR is small (∼2%) in the global average, but significant in particular geographical regions.

Climate change and child malnutrition: A Nigerian perspective

Erratic temperatures and precipitation influence nutrition, human capital investment, and living standards, particularly for children. This study investigates the effect of climate change (changes in the monthly maximum average near-surface temperature and total monthly precipitation) on children’s health outcomes, particularly stunting and underweight, in Nigeria. We combine Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) data with high resolution gridded climate data. We find that the rise in temperature is associated with higher levels of stunting – even more so in rural areas. The paper’s findings highlight the need for climate-friendly policies to mitigate the long-term effect of climate change on malnourishment. Without such policies, climate change could reverse years of progress in lowering children’s malnutrition.

Amplified wintertime Barents Sea warming linked to intensified Barents oscillation

In recent decades, the Barents Sea has warmed more than twice as fast as the rest of the Arctic in winter, but the exact causes behind this amplified warming remain unclear. In this study, we quantify the wintertime Barents Sea warming (BSW, for near-surface air temperature) with an average linear trend of 1.74 °C decade−1 and an interdecadal change around 2003 based on a surface energy budget analysis using the ERA5 reanalysis dataset from 1979–2019. Our analysis suggests that the interdecadal change in the wintertime near-surface air temperature is dominated by enhanced clear-sky downward longwave radiation (CDLW) associated with increased total column water vapor. Furthermore, it is found that a mode of atmospheric variability over the North Atlantic region known as the Barents oscillation (BO) strongly contributed to the BSW with a stepwise jump in 2003. Since 2003, the BO turned into a strengthened and positive phase, characteristic of anomalous high pressure over the North Atlantic and South of the Barents Sea, which promoted two branches of heat and moisture transport from southern Greenland along the Norwegian Sea and from the Eurasian continent to the Barents Sea. This enhanced the water vapor convergence over the Barents Sea, resulting in BSW through enhanced CDLW. Our results highlight the atmospheric circulation related to the BO as an emerging driver of the wintertime BSW through enhanced meridional atmospheric heat and moisture transport over the North Atlantic Ocean.

How skillful was the projected temperature over China during 2002–2018?

Climate change has attracted significant attention due to its increasing impacts on various aspects of the world, and future climate projections are of vital importance for associated adaptation and mitigation, particularly at the regional scale. However, the skill level of the model projections over China in the past more than ten years remains unknown. In this study, we retrospectively investigate the skill of climate models within the Third (TAR), Fourth (AR4), and Fifth (AR5) Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC) for the near-term projections of near-surface (2 m) air temperature changes in China. Those models are revealed to be skillful in projecting the subsequent climatology and trend of the temperature changes in China during 2002–2018 from several to ten years ahead, with higher scores for the climatology than for the trend. The model projections display cold biases against observations in most of China, while the nationally averaged trend is overestimated by TAR models during 2002–2018 but underestimated by AR4 models during 2008–2018. For all emission scenarios, there is no obvious difference between the equal- and unequal-weighted averages based on the arithmetic averaging and reliability ensemble averaging method respectively, however the uncertainty range of projection is narrowed after weighting. The near-term temperature projections differ slightly among various emission scenarios for the climatology but are largely different for the trend.