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It’s important to understand the role of permafrost in the future climate and water resources management, for huge storage of soil organic carbon and ground ice in the permafrost. To date, large uncertainties still exist in permafrost simulations for many reasons. One reason is being a lack of long-term meteorological, permafrost and carbon observations. Here, we therefore present datasets for air temperatures, precipitation, soil temperature and moisture, active layer thickness, ground temperatures at different depths, soil organic carbon contents, and ecosystem carbon emission rates for the Qilian Mountains of the Northeastern Qinghai-Tibetan Plateau during 2011–2020. The data come from 5 automatic meteorological stations, 21 boreholes with depths from 11.5 to 149.3 m, and 12 active layer monitoring sites, which are used to obtain the hydrothermal and thermal states, and climate change in the study area. Soil organic carbon contents is available from 10 deep boreholes, down to a depth of 20 m. Ecosystem respiration rates are obtained from the prevalent vegetation types of alpine wet meadow, meadow, and steppe for the growing seasons. This decade’s high-quality datasets are expected to serve as useful inputs for earth system models, and are for researchers working in those disciplines including geophysics, ecology, and hydrology in alpine environments. The datasets are available from the National Tibetan Plateau/Third Pole Environment Data Center and can be downloaded from http://dx.doi.org/10.11888/Cryos.tpdc.272840 (Mu and Peng, 2022).

The high latitudes are a hotspot for past and future climate change as forced climate signals have begun to emerge from internal variability in recent decades. New tools, such as initial condition large ensembles, provide a simulated range of possible climate realities that allow for separating the externally forced and internally variable components of the climate system. In addition, interactions between environmental variables and atmospheric circulation patterns can be detected in an unforced climate scenario and removed to isolate thermodynamic influences on the climate system. In the Arctic, this separation between dynamic and thermodynamic influences can be used to examine the impact of permafrost degradation on surface air temperatures (SAT). While impacts from permafrost degradation and subsequent carbon release have been thoroughly studied, geophysical influences have not received as much attention. This study employs the Community Earth System Model’s Large Ensemble to simulate and analyze these geophysical impacts over three time periods: 1976–2005, 2021–2050, and 2071–2100. As soil is thawing earlier and freezing later, we focus on spring and autumn to determine permafrost’s thermodynamic influence on SAT across Eurasia. We find that large internal variability, primarily due to atmospheric dynamics, affects spring SATs through 2100 while variability in autumn SATs will decrease over time due to increasing thermodynamic surface factors. These thermodynamic surface influences are most prominent in areas of continuous and discontinuous permafrost and lesser in non-permafrost regions, likely the result of a changing seasonal surface energy budget resulting from degradation and loss of permafrost.

As Arctic sea ice declines in response to climate change, a shift from thick multiyear ice to a thinner ice cover is occurring. With this transition, ice thicknesses approach a threshold below which ice no longer insulates the atmosphere from oceanic surface fluxes. While this is well known, there are no estimates of the magnitude of this threshold, nor of the proportion of sea ice area that is below this threshold as ice thins. We determine this threshold by simulating the atmospheric response to varying thicknesses, ranging from 0.0 to 2.0 m and determine that threshold to be 0.40–0.50 m. The resulting “effective” ice area is 4–14% lower than reported total ice area, as 0.39–0.97 × 106 km2 of the total ice area falls below the threshold throughout the twentieth century, including during notable ice minima. The atmosphere above large non-insulating ice-covered regions is susceptible to more than 2 °C of warming despite ice presence. Observed mean Arctic Ocean ice thickness is projected to fall below this threshold as early as the mid-2020s. Studies on ocean–atmosphere interactions in relation to sea ice area should focus on this insulating sea ice area, where ice is at least 0.40–0.50 m thick, and treat ice regions below 0.40–0.50 m thickness with caution.

Air temperature is an important indicator of climate change, as well as for understanding changes in hydrology, ecology, and other natural systems. However, meteorological stations that provide reliable temperature observations are usually sparse in areas of complex terrain, thus limiting our ability to quantify high spatial resolution temperature variability in these regions. Here, we apply three statistical downscaling methods to daily air temperature output from the sixth Coupled Model Intercomparison Project (CMIP6), validated with 22 meteorological stations over the Qilian Mountains. Based on different downscaling methods, we find RMSE and MAE are reduced as much as 59–66%, with the ratio of RMSE and MAE to the annual average temperature of stations decreasing from 147.9% to 61.0% and 143.3% to 64.7%, respectively, depending on the method. Compared to the original data, annual temperature based on the best downscaling methods differed by −2.85±3.61°C during the historical 1850–2014 period and, for the 2015–2100 projections, by 2.13±3.30°C for SSP1-2.6, −2.13±3.29°C for SSP2-4.5, −2.11±3.24°C for SSP3-7.0, and −2.12±3.23°C for SSP5-8.5. The downscaled annual air temperatures show a warming trend ranging 0.15­0.22°C/10yr for the historical experiment, 0.08°C­0.14°C/10yr for SSP1-2.6, 0.24­0.35°C/10yr for SSP2-4.5, 0.43­0.63°C/10yr for SSP3-7.0, and 0.52­0.76°C/10yr for SSP5-8.5 in the Qilian Mountain. These results indicate that the accuracy of the downscaled temperatures is improved compared to the original data. However, we also find that, compared with the downscaled data, the original projections have been overestimated.

The cryosphere plays a major role in earth's climate system. Most cryospheric assessments focus on one or more of its components and their response to climate change. However, to date, there has not been a comprehensive evaluation of the entire global cryosphere. We therefore determine such a holistic estimate and quantify changes to the hemispheric and global cryosphere due to climate change, by synthesizing sea ice, snow cover, and frozen ground extents into one global cryospheric extent data set. The 1981–2010 climatology of daily global cryospheric extent ranges from 45.7 ± 0.7 × 106 to 87.2 ± 2.0 × 106 km2 (9.0%–17.1%), from 13.3 ± 0.8 × 106–66.3 ± 1.7 × 106 km2 (5.2%–26.0%) in the Northern Hemisphere (NH), and from 17.9 ± 0.3 × 106 to 33.6 ± 0.4 × 106 km2 (7.0%–13.2%) in the Southern Hemisphere (SH). The monthly maximum cryospheric extent of 85.84 ± 1.91 × 106 km2 occurs in December, whereas minimum occurs in July with 45.92 ± 0.70 × 106 km2. During 1979–2016, global cryospheric area extent lost approximately 87 ± 11 × 103 km2/yr, with a decrease of 102 ± 9.7 × 103 km2/yr in the NH that was partly offset by an increase of 14.6 ± 4.4 × 103 km2/yr in the SH. The first day of cryospheric cover was delayed by 3.6 days at a rate of 0.95 days/decade, and the last day advanced by 5.7 days, at a rate of 1.5 days/decade. The duration and number of cryospheric cover days decreased by 8.7 days and 7.6 days over the study period, respectively. These variations of global cryospheric extent are correlated with rising air temperatures. Our findings highlight the importance of assessing the cryosphere as a whole, and provide a way to quantitatively estimate its overall changes.

Soil freeze depth (SFD) is an important indicator of cryospheric and climate change. Changes in SFD have important effects on hydrology, the energy balance, carbon exchange, and ecosystem diversity. However, quantifying and predicting SFD at large scales remains a challenge due to sparse long-term observations. This study employs the Stefan solution combined with 16 of the coupled model inter-comparison project phase 5 (CMIP5) models over the historical period (1850–2005) and three representative concentration pathways (RCP 2.6, 4.5, and 8.5) for 2006–2100, the Climatic Research Unit dataset (1901–2013), and hundreds of soil temperature, air temperature, precipitation, and snow depth sites to analyze the spatiotemporal variability of SFD in Eurasia under historical and projected climate change. During 1850–2005, a statistically significant SFD decrease of 0.49 ± 0.04 cm/decade is observed. Spatially, the biggest decreases are generally in Siberia and on the Tibetan Plateau. There is a projected decrease in 2006–2100 SFD of 4.58 ± 0.26, 1.85 ± 0.21, and 0.45 ± 0.18 cm/decade for RCP 8.5, 4.5, 2.6, respectively. These variations in SFD provide key insights into spatiotemporal changes in climate, and facilitate improved understanding of variation in frozen ground across Eurasia.

The Rainy and Dry Seasons (RADS) dataset, a new compilation of precipitation statistics available to the public, is described. The dataset contains the dates of onset and demise of the rainy season (one date per year), the duration of the rainy and dry seasons, and the accumulated precipitation during the rainy and dry seasons. The methodology for detecting the characteristics of the rainy season is based solely on precipitation data. RADS was developed from multiple global gridded daily precipitation datasets [Tropical Rainfall Measuring Mission (TRMM), 1998–2015; Climate Prediction Center Unified Gauge-Based Analysis of Global Daily Precipitation (CPC_UNI), 1979–present; and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), 1980–present] and therefore shares the spatial resolution, temporal range, and limitations of the original precipitation datasets. This is the first free public dataset of the characteristics of the rainy and dry seasons created using a consistent methodology across the globe, including all major monsoonal regions. We expect that the RADS dataset will contribute to our understanding of the sources of variability of the timing of rainy seasons (on local to regional scales) and monsoons (on large scales) and their impacts on water resource management and other aspects of geosciences and human activities.

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

Rising temperatures result in permafrost degradation in cold regions, but with considerable spatial heterogeneity due to local factors including peat. Peat plays an important role in the permafrost carbon cycle by impacting the soil thermal and hydrologic characteristics. Here we use field observations to describe the role of peat on permafrost thaw based on comparisons at two sites: EBoTA characterized by primarily peat, and PT5 with more mineral soils. Compared to PT5, soil temperature at the EBoTA site was lower in the warm season, but higher in the cold season. Freezing onset was generally 0–19 days later at the PT5 site compared to EBoTA throughout the soil column, except at the 0.1 m depth where it occurred 11 days earlier. The average thawing onset at PT5 was 18–96 days earlier. There were 20–119 more freezing days and 19–118 fewer thawing days at EBoTA than at PT5, and the zero annual amplitude and active layer thickness were lower at EBoTA. Finally, the mean annual ground temperature was greater at PT5, and was even above 0 °C in three recent years. These results illustrate peat’s thermal insulation effect on permafrost due to its low thermal conductivity and high moisture content. Snow depth increases also amplify the insulation effect. These field observations therefore not only verify, but also quantify the protective role of peatlands on permafrost.

Permafrost degradation leads to the release of organic carbon to the atmosphere, and in turn, affects climate change. The amount of organic carbon released to the atmosphere depends on the thawing rate of peat-rich permafrost. However, the exact mechanism of peat-rich permafrost thawing is still unclear. Here we choose two locations on the northeast Tibetan Plateau: the peat-rich EBoTA (“peatland”) site, and the mineral-rich PT5 (“mineral”) site. We use the Geophysical Institute Permafrost Laboratory model to reveal the response of permafrost to warming climate from 1850 to 2100 applying different shared socioeconomic pathways (SSPs). Monthly soil temperature increased during the historical and future periods at both sites, but a slower trend is evident at the peatland site relative to the mineral site. Seasonally, the warming trend is faster in winter than that in summer at both sites. Active layer deepening at the mineral site is 3–12 times that of the peatland site. In addition, permafrost will persist until 2100 in all but one of the SSPs at the peatland site. At the mineral site, all permafrost will degrade to seasonally frozen ground, with only taliks remaining for all future scenarios. These changes indicated a slower response of peat-rich permafrost to climate change, due to peat's low thermal conductivity and high soil moisture. These results could be useful in permafrost carbon studies.

Variability of active layer thickness (ALT) in permafrost regions is critical for assessments of climate change, water resources, and engineering applications. Detailed knowledge of ALT variations is also important for studies on ecosystem, hydrological, and geomorphological processes in cold regions. The primary objective of this study is therefore to provide a comprehensive 1971–2000 climatology of ALT and its changes across the entire Northern Hemisphere from 1850 through 2100. To accomplish this, in situ observations, the Stefan solution based on a thawing index, and the edaphic factor (E factor) are employed to calculate ALT. The thawing index is derived from (i) the multimodel ensemble mean of 16 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) over 1850–2005, (ii) three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5) for 2006–2100, and (iii) Climatic Research Unit (CRU) gridded observations for 1901–2014. The results show significant spatial variability in in situ ALT that generally ranges from 40 to 320 cm, with some extreme values of 900 cm in the Alps. The differences in the ALT climatology between the three RCPs and the historical experiments ranged from 0 to 200 cm. The biggest increases, of 120–200 cm, are on the Qinghai–Tibetan Plateau, while the smallest increases of less than 20 cm are in Alaska. Averaged over all permafrost regions, mean ALT from CMIP5 increased significantly at 0.57 ± 0.04 cm decade−1 during 1850–2005, while 2006–2100 projections show ALT increases of 0.77 ± 0.08 cm decade−1 for RCP2.6, 2.56 ± 0.07 cm decade−1 for RCP4.5, and 6.51 ± 0.07 cm decade−1 for RCP8.5.

Vegetation is closely tied to climate change, hydrological processes, the carbon cycle, and the energy balance. However, in cold regions, both climate and vegetation changes are also closely coupled to permafrost. The association between amplified warming and greening in Northern Hemisphere permafrost regions is not clearly understood. We therefore produce an extended 1982-2015 normalized difference vegetation index record based on concatenated Global Inventory Modeling and Mapping Studies and Moderate-Resolution Imaging Spectroradiometer data to quantify the spatiotemporal patterns of vegetation variability. We also establish spatiotemporal permafrost warming patterns based on a thawing index, active layer thickness, soil temperature, the first thaw date, and thawing days. Next, we establish the association between vegetation and warming permafrost. We find that normalized difference vegetation index increases in approximately 70% of Northern Hemisphere permafrost regions during the growing season (April-October) and in 72% of the region during autumn. Warming permafrost shows a positive relationship with greening. A higher thawing index, greater active layer thickness, higher soil temperature, and also increased precipitation are linked with the observed greening. An earlier first thaw date and an increased number of thawing days also correlate with vegetation greening. These findings underscore the sensitivity of vegetation to warming permafrost. Continued permafrost warming will result in further greening of Northern Hemisphere cold regions (and vice versa), with important implications for the permafrost soil-vegetation-atmosphere carbon cycles, and the water, nutrient, and energy budgets.

Source at https://doi.org/10.1017/S0032247418000645. A lasting legacy of the International Polar Year (IPY) 2007–2008 was the promotion of the Permafrost Young Researchers Network (PYRN), initially an IPY outreach and education activity by the International Permafrost Association (IPA). With the momentum of IPY, PYRN developed into a thriving network that still connects young permafrost scientists, engineers, and researchers from other disciplines. This research note summarises (1) PYRN’s development since 2005 and the IPY’s role, (2) the first 2015 PYRN census and survey results, and (3) PYRN’s future plans to improve international and interdisciplinary exchange between young researchers. The review concludes that PYRN is an established network within the polar research community that has continually developed since 2005. PYRN’s successful activities were largely fostered by IPY. With >200 of the 1200 registered members active and engaged, PYRN is capitalising on the availability of social media tools and rising to meet environmental challenges while maintaining its role as a successful network honouring the legacy of IPY.

Variations in seasonal soil freeze/thaw state are important indicators of climate change and influence ground temperature, hydrological processes, surface energy, and the moisture balance. Previous studies mainly focused on the active layer and permafrost, while seasonally frozen ground research in nonpermafrost regions has received less attention. In this study, we investigate the response of changes in seasonal soil freeze/thaw state to changes in air temperatures by combining observations from more than 800 stations with gridded mean monthly air temperature data across China. The results show that mean annual air temperature (MAAT) increased statistically significantly by 0.29 ± 0.03°C/decade from 1967 to 2013, with greater warming on the Qinghai-Tibetan Plateau. There is a statistically significant decrease in the freeze/thaw cycle (FTC) at 0.39 ± 0.05 cycles/decade. In addition, there are strong negative correlations between FTC and MAAT. Estimating the soil freeze/thaw state classification based on the number of days in the month, we find that changes of mean annual area extent of seasonal soil freeze/thaw state decreased significantly for completely frozen (CF) ground, while the area extent of partially frozen (PF) and unfrozen (UF) ground both increased. Changes in mean monthly area extent of seasonal soil freeze/thaw state indicate that the extent of CF and UF area was decreasing and increasing, respectively. But for the extent of PF areas, both increasing and decreasing trends were observed. Quantifying the spatial pattern of the seasonal soil freeze/thaw, we find that CF and PF areas are located in northern China and the Tibetan Plateau from December to March, and UF areas are located in southern China. The variations of mean annual area extent departure of soil freeze/thaw states are consistent with MAAT changes in different land cover types across China.

Regions of strong land–atmosphere coupling are often depicted as static in time. However, the mechanisms through which the land surface impacts atmospheric conditions vary on sub-daily to multi-decadal timescales. Therefore, characterizing the long-term variability of land–atmosphere interactions may provide a means of predicting when surface-induced extreme temperature events will be more or less likely to occur. We evaluate the coupling strength between soil moisture, as represented by in situ observations and 1-month Standardized Precipitation Index (SPI), and subsequent monthly maximum temperature (TMAX) over the contiguous United States. We find that the utility of SPI as a proxy for soil moisture anomalies is limited to the summer, as the correlations between SPI and TMAX are not significantly related in the other seasons. We examine the variability in summer SPI–TMAX coupling in four regions of the United States. In general, we find the strongest relationships between SPI and TMAX in the Southern Great Plains. However, our results demonstrate that the coupling strength varies considerably over time in most regions of the United States. The long-term variability in SPI–TMAX coupling strength is strongly related to the Pacific Decadal Oscillation in the northwest and midwest United States, and the Atlantic Multidecadal Oscillation in the southeast United States The results of this study suggest that land–atmosphere coupling in the contiguous United States is modulated by multi-decadal oscillations in Pacific and Atlantic sea surface temperatures.

Urbanization plays an important role in human-induced climate change at the regional scale through altered land-atmosphere interactions over urban areas. In this study, the impacts of future urbanization in China on climate are investigated. The Weather Research and Forecasting model is used to downscale future projections using Representative Concentration Pathways (RCP) 4.5 simulations from the Community Earth System Model. Results for 2050 show decreased latent and increased sensible heat fluxes over the urban area, therefore leading to higher surface temperatures and less humidity. Future climate projections reveal that urbanization produces strong warming effects, up to 1.9 °C at regional and local/urban scales, which is comparable to the magnitude of greenhouse gas forcing under the RCP 4.5 scenario. Greater urban warming effects are projected during night and summer, which can be attributed to the high heat capacity of built-up areas. The impacts of urbanization on precipitation show varying effects primarily in summer—both increases and decreases depending on spatial scale—related to both local moisture deficits and large-scale circulation changes. Urbanization could strengthen the East Asian summer monsoon in southern China in summer, and slightly weaken it in eastern China in winter. Due to these significant impacts, we suggest that urbanization should be included in model projections to provide a more realistic and complete depiction of future climate.

Atmospheric water demand affects a variety of factors, including primary production and the terrestrial water balance. Precipitation gradients from arid to humid regions also impact the water balance and play a large role in vegetation dynamics. Focusing on a 23-year period (1989–2011), we examine precipitation during the growing season in conjunction with the Normalized Difference Vegetation Index (NDVI) series for 21 satellite scenes spanning across the southwestern United States. We classify the satellite scenes into three different groups, based on the United Nations Aridity Index (AI). Group 1 is categorized as relatively humid with AI ≥ 0.65, group 2 is intermediate with 0.50 ≤ AI 3 mm and >13 mm) changed during this time. We also use cross-correlation analyses to establish the lagged behavior of the three types of vegetation in relation to precipitation amount and number of events. The vegetation response is similar between precipitation amount and number of precipitation events. However, in the arid region, we find distinct responses to precipitation depending on the vegetation type. The magnitude and significance of the vegetation response to precipitation patterns increase with environmental aridity. There is thus a meaningful disparity of vegetation behavior in time and space.

Tropical Rainfall Measuring Mission Precipitation Radar precipitation features are analyzed to understand the role of storm characteristics on the seasonal and diurnal cycles of precipitation in four distinct regions in Costa Rica. The distribution of annual rainfall is highly dependent on the stratiform precipitation, driven largely by seasonal increases in stratiform area. The monthly distribution of stratiform rain is bimodal in most regions, but the timing varies regionally and is related to several important large-scale features: the Caribbean low-level jet, the ITCZ, and the Chorro del Occidente Colombiano (CHOCO) jet. The relative importance of convective precipitation increases on the Caribbean side during wintertime cold air surges. Except for the coastal Caribbean domain, most regions show a strong diurnal cycle with an afternoon peak in convection followed by an evening increase in stratiform rain. Along the Caribbean coast, the diurnal cycle is weaker, with evidence of convection associated with the sea breeze, as well as a nocturnal increase in storms. The behavior of extreme precipitation features with rain volume in the 99th percentile is also analyzed. They are most frequent from May to November, with notable differences between features at the beginning/end of the rainy season versus those in the middle, as well as between wet and dry seasons. Convective rain exceeds stratiform in winter and midsummer extreme features, while stratiform rain is larger at the beginning and end of the wet season. Given projected changes in precipitation and extreme events in Costa Rica for future climate change scenarios, the results indicate the importance of understanding both changes in total precipitation and in the storm characteristics.

Limited research has been performed examining the relationships between southeast US wildfire and weather type patterns using modern techniques and data sets. The purpose of this article is to examine the relationship between wildfire ignitions in the central Gulf Coast, United States and weather type occurrences in an effort to identify regional patterns associated with wildfire ignitions. Wildfire occurrence data are obtained for nine national forests in the central Gulf Coast for the period 1970 to 2011. The Spatial Synoptic Classification scheme (SSC) is used to classify daily weather type variations for each national forest. Years with high numbers of ignitions experience statistically higher numbers of dry tropical (DT) weather types than years with low numbers of ignitions in five of nine national forests. These differences range from 17 to 160% more DT days during periods of high ignitions. Statistically significant positive Spearman rank correlations exist between annual number of ignitions and annual number of DT days in a given forest. While understanding such relationships on an annual scale is useful, it says nothing about the actual timing of weather types leading up to an ignition. An analysis of weather type activity 30, 90, and 180 days prior to ignitions reveals that the median ratio of DT days to moist moderate (MM) and moist tropical (MT) days is statistically higher for periods leading up to a fire. Positive phases of the North Atlantic Oscillation (NAO) result in an increased number of DT days. This confirms previous studies that have suggested the strength of the westerlies increases during the positive phase of the NAO, driving DT weather types eastward from their southwest US/Mexico source region. We can therefore also conclude that the positive phase of the NAO is linked with increased wildfire activity in the central Gulf Coast, United States.

Precipitation throughfall (TF) plays an important role in the water balance of tropical forests. This study used 164 gauges to quantify precipitation and TF variability in a tropical pre-montane transitional cloud forest on the Caribbean slope of the Cordillera Tilaran, Costa Rica, to identify the ecological and meteorological drivers of this variability. Daily TF measurements were taken from 28 June to 17 July 2012 and 12 June to 16 July 2013, for a total of 39 precipitation events. The total mean TF was 87.9 percent and TF at individual gauges ranged from 22.7 percent to 245.7 percent. Leaf area index (LAI) was calculated above each gauge using hemispheric photography for a mean study-site LAI of 7.7. There was no statistically significant relationship between LAI and TF. However, the amount of TF was positively correlated with precipitation intensity, while the variability of TF was negatively correlated with precipitation intensity. Our calculations indicate that at least 61 gauges are required to obtain mean TF estimates with less than 5 percent error. This study demonstrates that TF is highly spatially heterogeneous due to multiple compounding effects. RESUMEN Precipitacion interna a traves del dosel del bosque (TF) es una parte importante del equilibrio de agua en bosques tropicales. Este estudio tuvo como objetivo cuantificar la variabilidad de precipitacion y TF en un bosque nuboso transicional y premontano en el lado Caribe de la Cordillera Tilaran, especificamente en el Centro Soltis, Costa Rica, e identificar los factores ecologicos y meteorologicos en esta variabilidad. Mediante 164 pluviometros, se obtuvieron lecturas diarias de precipitacion interna, desde 28 Junio –17 Julio 2012 y 12 Junio–16 Julio 2013, abarcando 39 eventos pluviales. El promedio total de la precipitacion interna fue 87.9% y la precipitacion interna de los pluviometros fluctuaron entre 22.7% y 245.7%. Mediante fotografia hemisferica se calcularon los indices de area foliar (LAI) sobre cada pluviometro. El promedio del LAI fue 7.7. No hubo una relacion estadisticamente significativa entre LAI y TF; sin embargo, hubo una correlacion positiva entre la precipitacion interna y la intensidad de la precipitacion, y la variabilidad de la precipitacion interna correlaciono negativamente con la intensidad de la precipitacion. Nuestros resultados indican que al menos 61 pluviometros son necesarios para determinar los promedios de precipitacion interna con menos de 5% de error. Este estudio demuestra que la precipitacion interna es espacialmente heterogenea debido a muchos efectos amontonados.

Historical temperature variability over China during the twentieth century and projected changes under three emission scenarios for the twenty-first century are evaluated on the basis of a multimodel ensemble of 20 GCMs from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and two observational datasets. Changes relative to phase 3 of the Coupled Model Intercomparison Project (CMIP3) are assessed, and the performance of individual GCMs is also quantified. Compared with observations, GCMs have substantial cold biases over the Tibetan Plateau, especially in the cold season. The timing and location of these biases also correspond to the greatest disagreement among the individual models, indicating GCMs’ limitations in reproducing climatic features in this complex terrain. The CMIP5 multimodel ensemble shows better agreement with observations than CMIP3 in terms of the temperature biases. Both CMIP3 and CMIP5 capture the climatic warming over the twentieth century. However, the magnitude of the annual mean temperature trends is underestimated. There is also limited agreement in the spatial and seasonal patterns of temperature trends over China. Based on six statistical measures, four individual models—the Max Planck Institute Earth System Model, low resolution (MPI-ESM-LR), Second Generation Canadian Earth System Model (CanESM2), Model for Interdisciplinary Research on Climate, Earth System Model (MIROC-ESM), and Community Climate System Model, version 4 (CCSM4)—best represent surface air temperature variability over China. The future temperature projections indicate that the representative concentration pathway (RCP) 8.5 and RCP 4.5 scenarios exhibit a gradual increase in annual temperature during the twenty-first century at a rate of 0.60° and 0.27°C (10 yr)−1, respectively. As the lowest-emission mitigation scenario, RCP 2.6 projects the lowest rate of temperature increase [0.10°C (10 yr)−1]. By the end of the twenty-first century, temperature is projected to increase by 1.7°–5.7°C, with larger warming over northern China and the Tibetan Plateau.

Precipitation variability has great economic, social, and environmental impacts across the globe, and in particular in China. This paper evaluates the historical precipitation variability based on 20 general circulation models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive over the 20th century relative to two observational data sets and quantifies CMIP5 improvements over CMIP3. Multimodel ensemble means and individual models are assessed. Three future emission scenarios are used (representative concentration pathways (RCP) 8.5, RCP 4.5, and RCP 2.6), and 21st century CMIP5 estimates are put into context based on the 20th century biases. We find that CMIP5 models can reproduce the spatial pattern of precipitation over China during the 20th century, which represents an improvement over CMIP3. However, the models overestimate the magnitude of seasonal and annual precipitation in most regions of China, especially along the eastern edge of the Tibetan Plateau, and underestimate summer precipitation over southeastern China. For China as a whole, CMIP5's overestimation of annual precipitation is greater than CMIP3, which can be traced back to a greater underestimation of summer precipitation in CMIP3. There is a large spread among individual models, with the greatest uncertainties in simulating summer precipitation. Trends and correlations also suggest a better agreement of CMIP5 with observations than CMIP3. Throughout the 20th century, both the observations and models show an increasing trend in precipitation over parts of northwestern China and a decreasing trend over the Tibetan Plateau. There is poor agreement in precipitation trends over the southeast and northeast regions. In general, multimodel means cannot capture the amplitude of observed multidecadal precipitation variability. In the 21st century, precipitation is generally projected to increase across all of China under all three scenarios. RCP 8.5 exhibits the largest significant trend at a rate of +1.5 mm/yr, corresponding to 16% precipitation increase by the end of the century. The RCP 2.6 scenario shows the smallest increases, at +0.5 mm/yr (6%) by 2100. The greatest increases are projected to occur over the Tibetan Plateau and eastern China in summer, suggesting an altered monsoonal circulation in the future. However, due to the uncertainties in CMIP5, future precipitation projections should be interpreted with caution.

With climate change, precipitation variability is projected to increase. The present study investigates the potential interactions between watershed characteristics and precipitation variability. The watershed is considered as a functional unit that may impact seasonal precipitation. The study uses historical precipitation data from 370 meteorological stations over the last five decades, and digital elevation data from regional watersheds in the southwestern United States. This domain is part of the North American Monsoon region, and the summer period (June–July–August, JJA) was considered. Based on an initial analysis for 1895–2011, the JJA precipitation accounts, on average, for 22–43% of the total annual precipitation, with higher percentages in the arid part of the region. The unique contribution of this research is that entropy theory is used to address precipitation variability in time and space. An entropy-based disorder index was computed for each station’s precipitation record. The JJA total precipitation and number of precipitation events were considered in the analysis. The precipitation variability potentially induced by watershed topography was investigated using spatial regionalization combining principal component and cluster analysis. It was found that the disorder in precipitation total and number of events tended to be higher in arid regions. The spatial pattern showed that the entropy-based variability in precipitation amount and number of events gradually increased from east to west in the southwestern United States. Regarding the watershed topography influence on summer precipitation patterns, hilly relief has a stabilizing effect on seasonal precipitation variability in time and space. The results show the necessity to include watershed topography in global and regional climate model parameterizations.

[1] Sixty years of tropical cyclone precipitation (TCP) in Texas has been analyzed because of its importance in extreme hydrologic events and the hydrologic budget. We developed multiple linear regression models to provide seasonal forecasts for annual TCP, TCP's contribution (percentage) to total precipitation, and the number of TCP days in Texas. The regression models are based on three or fewer predictors with model fits ranging from 0.18 to 0.43 (R2) and cross-validation accuracy of 0.05–0.36 (R2). La Nina exhibits the most important control on TCP in Texas. It is the major driver in our models and acts to reduce the vertical shear in the Caribbean and the tropical Atlantic, thereby generating more precipitating storms in Texas. Lower maximum potential velocity, the theoretical maximum wind speed that storms can attain, in the Gulf of Mexico, and low-level vorticity in the Atlantic hurricane main development region increased the modeled R2 by 20% or more. Both variables have negative coefficients in the TCP models. Lower maximum potential velocity and vorticity are associated with tropical cyclones with lower maximum wind speed and slower translation speed. Such weak TCs produce the majority of TCP and extreme TCP events in Texas. The quartiles of the TCs with strongest maximum wind speed and fastest translation speed are not associated with the largest mean daily precipitation based on observations in Texas. We have also shown that sea level pressure in the Gulf of Mexico, sea surface temperature in the Caribbean, and the North Atlantic Oscillation are potentially important predictors of seasonal TCP in Texas.

Land cover changes (LCCs) play an important role in the climate system. Research over recent decades highlights the impacts of these changes on atmospheric temperature, humidity, cloud cover, circulation, and precipitation. These impacts range from the local- and regional-scale to sub-continental and global-scale. It has been found that the impacts of regional-scale LCC in one area may also be manifested in other parts of the world as a climatic teleconnection. In light of these findings, this article provides an overview and synthesis of some of the most notable types of LCC and their impacts on climate. These LCC types include agriculture, deforestation and afforestation, desertification, and urbanization. In addition, this article provides a discussion on challenges to, and future research directions in, assessing the climatic impacts of LCC.

The response of seasonal soil freeze depth to climate change has repercussions for the surface energy and water balance, ecosystems, the carbon cycle, and soil nutrient exchange. Despite its importance, the response of soil freeze depth to climate change is largely unknown. This study employs the Stefan solution and observations from 845 meteorological stations to investigate the response of variations in soil freeze depth to climate change across China. Observations include daily air temperatures, daily soil temperatures at various depths, mean monthly gridded air temperatures, and the normalized difference vegetation index. Results show that soil freeze depth decreased significantly at a rate of −0.18 ± 0.03 cm yr−1, resulting in a net decrease of 8.05 ± 1.5 cm over 1967–2012 across China. On the regional scale, soil freeze depth decreases varied between 0.0 and 0.4 cm yr−1 in most parts of China during 1950–2009. By investigating potential climatic and environmental driving factors of soil freeze depth variability, we find that mean annual air temperature and ground surface temperature, air thawing index, ground surface thawing index, and vegetation growth are all negatively associated with soil freeze depth. Changes in snow depth are not correlated with soil freeze depth. Air and ground surface freezing indices are positively correlated with soil freeze depth. Comparing these potential driving factors of soil freeze depth, we find that freezing index and vegetation growth are more strongly correlated with soil freeze depth, while snow depth is not significant. We conclude that air temperature increases are responsible for the decrease in seasonal freeze depth. These results are important for understanding the soil freeze–thaw dynamics and the impacts of soil freeze depth on ecosystem and hydrological process.

Several recommendations have been proposed for detecting land use and land cover change (LULCC) on the environment from, observed climatic records and to modeling to improve its understanding and its impacts on climate. Researchers need to detect LULCCs accurately at appropriate scales within a specified time period to better understand their impacts on climate and provide improved estimates of future climate. The US Climate Reference Network (USCRN) can be helpful in monitoring impacts of LULCC on near-surface atmospheric conditions, including temperature. The USCRN measures temperature, precipitation, solar radiation, and ground or skin temperature. It is recommended that the National Climatic Data Center (NCDC) and other climate monitoring agencies develop plans and seek funds to address any monitoring biases that are identified and for which detailed analyses have not been completed.

Climate models simulate an intensifying Arctic hydrologic cycle in response to climatic warming, however the role of surface-atmosphere interactions from degrading frozen ground is unclear in these projections. Using Modern-Era Retrospective Analysis for Research and Applications (MERRA) data in high-latitude Eurasia, we examine long-term variability in surface-atmosphere coupling as represented by the statistical relationship between surface evaporative fraction (EF) and afternoon precipitation. Changes in EF, precipitation and their statistical association are then related to underlying permafrost type and snow cover. Results indicate significant positive trends in July EF in the Central Siberian Plateau, corresponding to significant increases in afternoon precipitation. The positive trends are only significant over continuous permafrost, with non-significant or negative EF and precipitation trends over isolated, sporadic and discontinuous permafrost areas. Concurrently, increasing EF and subsequent precipitation are found to coincide with significant trends in May and June snowmelt, which potentially provides the moisture source for the observed enhanced latent heating and moisture recycling in the region. As climate change causes continuous permafrost to transition to discontinuous, discontinuous to sporadic, sporadic to isolated and isolated permafrost disappears, this will also alter patterns of atmospheric convection, moisture recycling and hence the hydrologic cycle in high-latitude land areas.

Atmospheric modification by anomalously dry or wet soils can both enhance and suppress convective activity. However, the local- and meso-scale feedbacks governing soil moisture–precipitation coupling are embedded within the larger synoptic-scale environment. Despite their importance, synoptic-scale atmospheric conditions are rarely considered in studies examining soil moisture-atmosphere interactions. We combine self-organizing maps (SOMs) of 500 hPa geopotential height, spatial synoptic classification, and Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model air mass trajectories to determine if the synoptic-scale environment affects the ability of the land surface to force unorganized convection in Oklahoma. We identify several synoptic patterns that significantly impact the frequency of unorganized convection. Synoptic patterns characterized by mid-level troughs over the Southern Great Plains are less frequently associated with unorganized convective events. These patterns exhibit cool air advection in the mid- and lower-levels of the atmosphere, and are linked to suppression of convective activity. The synoptic patterns characterized by 500 hPa ridging over the study region are more frequently associated with unorganized convective events. These patterns likely result in increased net radiation, vapor pressure deficit, and more homogenously dry soils. Unorganized convective events that occur during these synoptic conditions initiate preferentially over dry soils. We present evidence that the synoptic-scale environment can influence whether and how the land surface has an impact on convection.

Changes in the ground thermal regime in high-latitude cold regions have important consequences for surface and subsurface hydrology, the surface energy and moisture balance, carbon exchange, as well as ecosystem diversity and productivity. However, assessing these changes, particularly in light of significant atmospheric and terrestrial changes in recent decades, remains a challenge owing to data sparseness and discontinuous observations. The annual freezing and thawing index can be useful in evaluating permafrost and seasonally frozen ground distribution, has important engineering applications, and is a useful indicator of high-latitude climate change. The freezing/thawing index is generally defined based on daily observations, which are not readily available for many high-latitude locations. We thus employ monthly air temperatures, and provide an assessment of the validity of this approach. On the basis of a comprehensive relative error (RE) evaluation we find that our methodology introduces errors of less than 5% for most high-latitude land areas, and works well in many midlatitude regions as well. We evaluate a suite of gridded monthly temperature datasets and select the University of East Anglia's Climatic Research Unit (CRU) temperature product, available for 1901–2002. We are thus able to provide a continuous long-term 25 km × 25 km gridded Northern Hemisphere freezing/thawing index. Long-term climatologies of the freezing/thawing index delineate the cold regions of the Northern Hemisphere, as well as areas of seasonally frozen ground and permafrost. Objective trend analysis indicates that in recent decades, no significant changes have occurred in Russian permafrost regions; however, seasonally frozen ground areas are experiencing significant warming trends. Over North America, Canadian and Alaskan permafrost regions are experiencing a decrease in freezing index during the cold season, while coastal areas and eastern Canada are seeing significant increase in warm season thawing index. Copyright © 2006 Royal Meteorological Society.

A new and distinctly interdecadal signal in the climate of the Pacific Ocean has been uncovered by examining the coupled behavior of sea surface temperatures (SSTs) and Northern Hemisphere atmospheric circulation. This interdecadal Pacific signal (IPS) of ocean–atmosphere interaction exhibits a highly statistically significant interdecadal component yet contains little to no interannual (El Niño scale) variability common to other Pacific climate anomaly patterns. The IPS thus represents the only empirically derived, distinctly interdecadal signal of Pacific Ocean SST variability that likely also represents the true interdecadal behavior of the Pacific Ocean–atmosphere system. The residual variability of the Pacific’s leading SST pattern, after removal of the IPS, is highly correlated with El Niño anomalies. This indicates that by simply including an atmospheric component, the leading mode of Pacific SST variability has been decomposed into its interdecadal and interannual patterns. Although the interdecadal signal is unrelated to interannual El Niño variability, the interdecadal ocean–atmosphere variability still seems closely linked to tropical Pacific SSTs. Because prior abrupt changes in Pacific SSTs have been related to anomalies in a variety of physical and biotic parameters throughout the Northern Hemisphere, and because of the persistence of these changes over several decades, isolation of this interdecadal signal in the Pacific Ocean–atmosphere system has potentially important and widespread implications to climate forecasting and climate impact assessment.

Over the course of the 20th century, average annual precipitation for the contiguous USA has increased by nearly 10%. This increase has been described as being dominated by ‘disproportionate’ increases in extreme precipitation events. However, methodological constraints have confounded detailed interpretation of such results. Here, we briefly describe those limitations and re-evaluate the nature of the observed precipitation changes using a method that allows for a more accurate examination of changes in the proportion of precipitation delivered in extreme daily events. We focus our analysis only on the trends in precipitation on the 10 wettest days of the year and compare the trends observed on those days with the trend in overall precipitation. When averaged across the USA, we find that the precipitation trends on the 10 wettest days of the year are not significantly different from the trend in total overall precipitation. On a regional level, in the northeast and southeast there is some evidence that the rate of precipitation increase on the wettest days exceeds that of total precipitation, whereas in the rest of the country the precipitation on the wettest days is increasing at a rate less than the increase in total precipitation. Copyright  2004 Royal Meteorological Society.

The relationship between El Nino, the Southern Oscillation, and their joint effect (ENSO), and Northern Hemisphere 500 hPa midlatitude circulation for the 1946–1994 observational record is determined. Seasonally standardized vortex positions at a spatial resolution of 5° longitude are correlated with an equatorial Pacific sea surface temperature anomaly index and the Southern Oscillation Index (SOI). The 500 hPa circumpolar vortex is consistently expanded over the central Pacific during and for the entire year following warm ENSO periods. The vortex is contracted over North America concurrently with warm ENSO and to a lesser degree during the following season. Expansion/contraction variability is also evident over east Asia, although the linkages to warm or cold ENSO are weak. Sea surface temperatures have a stronger association with the vortex than does the SOI.

[1] The total extent of ice melt on the Greenland ice sheet has been increasing during the last three decades. The melt extent observed in 2007 in particular was the greatest on record according to several satellite-derived records of total Greenland melt extent. Total annual observed melt extent across the Greenland ice sheet has been shown to be strongly related to summer temperature measurements from stations located along Greenland's coast, as well as to variations in atmospheric circulation across the North Atlantic. We make use of these relationships along with historical temperature and circulation observations to develop a near-continuous 226 year reconstructed history of annual Greenland melt extent dating from 2009 back into the late eighteenth century. We find that the recent period of high-melt extent is similar in magnitude but, thus far, shorter in duration, than a period of high melt lasting from the early 1920s through the early 1960s. The greatest melt extent over the last 2 1/4 centuries occurred in 2007; however, this value is not statistically significantly different from the reconstructed melt extent during 20 other melt seasons, primarily during 1923–1961.