Correction of Inhomogeneities in Observed Land Surface Temperatures over China

2020 ◽  
Vol 33 (20) ◽  
pp. 8885-8902
Author(s):  
Jizeng Du ◽  
Kaicun Wang ◽  
Baoshan Cui ◽  
Shaojing Jiang
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Cmip5 Models ◽  
Near Surface ◽  
Land Surface Temperatures ◽  
Long Term Trends ◽  
The Difference

AbstractLand surface temperature Ts and near-surface air temperature Ta are two main metrics that reflect climate change. Recently, based on in situ observations, several studies found that Ts warmed much faster than Ta in China, especially after 2000. However, we found abnormal jumps in the Ts time series during 2003–05, mainly caused by the transformation from manual to automatic measurements due to snow cover. We explore the physical mechanism of the differences between automatic and manual observations and develop a model to correct the automatic observations on snowy days in the observed records of Ts. Furthermore, the nonclimatic shifts in the observed Ts were detected and corrected using the RHtest method. After corrections, the warming rates for Ts-max, Ts-min, and Ts-mean were 0.21°, 0.34°, and 0.25°C decade−1, respectively, during the 1960–2014 period. The abnormal jump in the difference between Ts and Ta over China after 2003, which was mentioned in existing studies, was mainly caused by inhomogeneities rather than climate change. Through a combined analysis using reanalyses and CMIP5 models, we found that Ts was consistent with Ta both in terms of interannual variability and long-term trends over China during 1960–2014. The Ts minus Ta (Ts − Ta) trend is from −0.004° to 0.009°C decade−1, accounting for from −3.19% to 5.93% (from −3.09% to 6.39%) of the absolute warming trend of Ts (Ta).

scholarly journals Evapotranspiration Partitioning in CMIP5 Models: Uncertainties and Future Projections

2019 ◽  
Vol 32 (10) ◽  
pp. 2653-2671 ◽  
Author(s):  
Alexis Berg ◽  
Justin Sheffield
Keyword(s):  
Land Surface ◽  
Stomatal Closure ◽  
Surface Air Temperature ◽  
Soil Evaporation ◽  
Cmip5 Models ◽  
Canopy Interception ◽  
Area Index ◽  
Near Surface ◽  
Relative Contribution ◽  
The Tropics

Abstract Evapotranspiration (ET) is a key process affecting terrestrial hydroclimate, as it modulates the land surface carbon, energy, and water budgets. Evapotranspiration mainly consists of the sum of three components: plant transpiration, soil evaporation, and canopy interception. Here we investigate how the partitioning of ET into these three main components is represented in CMIP5 model simulations of present and future climate. A large spread exists between models in the simulated mean present-day partitioning; even the ranking of the different components in the global mean differs between models. Differences in the simulation of the vegetation leaf area index appear to be an important cause of this spread. Although ET partitioning is not accurately known globally, existing global estimates suggest that CMIP5 models generally underestimate the relative contribution of transpiration. Differences in ET partitioning lead to differences in climate characteristics over land, such as land–atmosphere fluxes and near-surface air temperature. On the other hand, CMIP5 models simulate robust patterns of future changes in ET partitioning under global warming, notably a marked contrast between decreased transpiration and increased soil evaporation in the tropics, whereas transpiration and evaporation both increase at higher latitudes and both decrease in the dry subtropics. Idealized CMIP5 simulations from a subset of models show that the decrease in transpiration in the tropics largely reflects the stomatal closure effect of increased atmospheric CO2 on plants (despite increased vegetation from CO2 fertilization), whereas changes at higher latitudes are dominated by radiative CO2 effects, with warming and increased precipitation leading to vegetation increase and simultaneous (absolute) increases in all three ET components.

Long-term trends and extremes in observed daily precipitation and near surface air temperature in the Philippines for the period 1951–2010

2014 ◽  
Vol 145-146 ◽  
pp. 12-26 ◽  
Author(s):  
Thelma A. Cinco ◽  
Rosalina G. de Guzman ◽  
Flaviana D. Hilario ◽  
David M. Wilson
Keyword(s):  
Air Temperature ◽  
Daily Precipitation ◽  
Surface Air Temperature ◽  
The Philippines ◽  
Near Surface ◽  
Long Term Trends

scholarly journals Construction of a surface air temperature series for Qingdao in China for the period 1899 to 2014

2018 ◽  
Vol 10 (1) ◽  
pp. 643-652
Author(s):  
Yan Li ◽  
Birger Tinz ◽  
Hans von Storch ◽  
Qingyuan Wang ◽  
Qingliang Zhou ◽  
...  
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Observation Data ◽  
China Meteorological Administration ◽  
Ascii Format ◽  
Long Term Trends ◽  
The City

Abstract. We present a homogenized surface air temperature (SAT) time series at 2 m height for the city of Qingdao in China from 1899 to 2014. This series is derived from three data sources: newly digitized and homogenized observations of the German National Meteorological Service from 1899 to 1913, homogenized observation data of the China Meteorological Administration (CMA) from 1961 to 2014 and a gridded dataset of Willmott and Matsuura (2012) in Delaware to fill the gap from 1914 to 1960. Based on this new series, long-term trends are described. The SAT in Qingdao has a significant warming trend of 0.11 ± 0.03 ∘C decade−1 during 1899–2014. The coldest period occurred during 1909–1918 and the warmest period occurred during 1999–2008. For the seasonal mean SAT, the most significant warming can be found in spring, followed by winter. The homogenized time series of Qingdao is provided and archived by the Deutscher Wetterdienst (DWD) web page under overseas stations of the Deutsche Seewarte (http://www.dwd.de/EN/ourservices/overseas_stations/ueberseedoku/doi_qingdao.html) in ASCII format. Users can also freely obtain a short description of the data at https://doi.org/https://dx.doi.org/10.5676/DWD/Qing_v1. And the data can be downloaded at http://dwd.de/EN/ourservices/overseas_stations/ueberseedoku/data_qingdao.txt.

scholarly journals REDCAPP (v1.0): parameterizing valley inversions in air temperature data downscaled from reanalyses

2017 ◽  
Vol 10 (8) ◽  
pp. 2905-2923 ◽  
Author(s):  
Bin Cao ◽  
Stephan Gruber ◽  
Tingjun Zhang
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Surface Effects ◽  
Model Parameters ◽  
Fine Scale ◽  
Mountain Areas ◽  
Near Surface ◽  
Cold Air ◽  
The Difference

Abstract. In mountain areas, the use of coarse-grid reanalysis data for driving fine-scale models requires downscaling of near-surface (e.g., 2 m high) air temperature. Existing approaches describe lapse rates well but differ in how they include surface effects, i.e., the difference between the simulated 2 m and upper-air temperatures. We show that different treatment of surface effects result in some methods making better predictions in valleys while others are better in summit areas. We propose the downscaling method REDCAPP (REanalysis Downscaling Cold Air Pooling Parameterization) with a spatially variable magnitude of surface effects. Results are evaluated with observations (395 stations) from two mountain regions and compared with three reference methods. Our findings suggest that the difference between near-surface air temperature and pressure-level temperature (ΔT) is a good proxy of surface effects. It can be used with a spatially variable land-surface correction factor (LSCF) for improving downscaling results, especially in valleys with strong surface effects and cold air pooling during winter. While LSCF can be parameterized from a fine-scale digital elevation model (DEM), the transfer of model parameters between mountain ranges needs further investigation.

scholarly journals Trends in Atmospheric Humidity and Temperature above Dome C, Antarctica Evaluated from Observations and Reanalyses

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 836
Author(s):  
Philippe Ricaud ◽  
Paolo Grigioni ◽  
Romain Roehrig ◽  
Pierre Durand ◽  
Dana E. Veron
Keyword(s):  
Surface Temperature ◽  
Global Climate ◽  
Microwave Radiometer ◽  
Southern Annular Mode ◽  
Warming Trend ◽  
Near Surface ◽  
Multidecadal Variability ◽  
Drying Trend ◽  
Long Term Trends

The time evolution of humidity and temperature above Dome C (Antarctica) has been investigated by considering data from (1) meteorological radiosondes (2005–2017), (2) the microwave radiometer HAMSTRAD (2012–2017), (3) four modern meteorological reanalyses (1980–2017) and (4) the southern annular mode (SAM) index (1980–2017). From these observations (2005–2017), a significant moistening trend (0.08 ± 0.06 kg m−2 dec−1) is associated with a significant warming trend (1.08 ± 0.55 K dec−1) in summer. Conversely, a significant drying trend of −0.04 ± 0.03 kg m−2 dec−1 (−0.05 ± 0.03 kg m−2 dec−1) is associated with a significant cooling trend of −2.4 ± 1.2 K dec−1 (−5.1 ± 2.0 K dec−1) in autumn (winter), with no significant trends in the spring. We demonstrate that 1) the trends identified in the radiosondes (2005–2017) are also present in the reanalyses and 2) the multidecadal variability of integrated water vapor and near-surface temperature (1980–2017) is strongly influenced by variability in the SAM index for all seasons but spring. Our study suggests that the decadal trends observed in humidity and near-surface temperature at Dome C (2005–2017) reflect the multidecadal variability of the atmosphere, and are not indicative of long-term trends that may be related to global climate change.

scholarly journals Comparisons of Time Series of Annual Mean Surface Air Temperature for China since the 1900s: Observations, Model Simulations, and Extended Reanalysis

2017 ◽  
Vol 98 (4) ◽  
pp. 699-711 ◽  
Author(s):  
Qingxiang Li ◽  
Lei Zhang ◽  
Wenhui Xu ◽  
Tianjun Zhou ◽  
Jinfeng Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Land Surface ◽  
Linear Trend ◽  
Surface Air Temperature ◽  
Climate Simulation ◽  
Cmip5 Models ◽  
Near Surface ◽  
Uncertainty Range ◽  
Homogeneity Assessment

Abstract Time series of global or regional average surface air temperature (SAT) are fundamental to climate change studies. A number of studies have developed several national and regional SAT series for China, but because of the diversity of the meteorological observational sites, the different quality control routines for processing the data, and the inconsistency of the statistical methods used, they differ in their long-term trends. This paper assesses the similarities and differences of the existing time series of the annual average SAT for China that are based upon historical meteorological observations since the 1900s. The results indicate that the China average is similar to the series for the Northern Hemisphere (NH) landmass, except that the initial warming of the NH series derived from the CRUTEM3/4 datasets, which represent global historical land surface air temperatures and near-surface air temperature anomalies over land, respectively, ends earlier (before the early 1940s) than in China’s series. A major difference among the existing China average time series is the 1940s warmth, a period when there were very few observations across the country because of World War II. The SAT anomalies for China during the 1930s to 1940s have been reduced by improved homogeneity assessment compared to previous estimates. The new improved time series is in better agreement with both the historical twentieth-century reanalysis data and the historical climate simulation of phase 5 of the Coupled Model Intercomparison Project (CMIP5) models. The new time series also shows the slowdown of the warming trend during the past 18 yr (1998–2015). The best estimate of a linear trend for increases in temperature with a 95% uncertainty range is 0.121° ± 0.009°C decade–1 for 1900–2015, indicating that the improved homogeneity assessment for China leads to a slightly greater trend than that based on raw data (0.107° ± 0.009°C decade–1).

scholarly journals REDCAPP (v1.0): Parameterizing valley inversions in air temperature data downscaled from re-analyses

2017 ◽  
Author(s):  
Bin Cao ◽  
Stephan Gruber ◽  
Tingjun Zhang
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Surface Effects ◽  
Model Parameters ◽  
Fine Scale ◽  
Mountain Areas ◽  
Near Surface ◽  
Cold Air ◽  
The Difference

Abstract. In mountain areas, the use of coarse-grid re-analysis data for driving fine-scale models requires downscaling of near-surface (e.g. 2 m high) air temperature. Existing approaches describe lapse rates well but differ in how they include surface effects, i.e. the difference between the simulated 2 m and upper-air temperatures. We show that different treatment of surface effects result in some methods making better predictions in valleys while others are better in summit areas. We propose the downscaling method REDCAPP (REanalysis Downscaling Cold Air Pooling Parameterization) with a spatially variable magnitude of surface effects. Results are evaluated with observations (395 stations) from two mountain regions and compared with three reference methods. Our findings suggest that the difference between near-surface air temperature and pressure-level temperature (∆T) is a good proxy of surface effects. It can be used with a spatially-variable Land-Surface Correction-Factor (LSCF) for improving downscaling results, especially in valleys with strong surface effects and cold air pooling during winter. While LSCF can be parameterized from a fine-scale digital elevation model (DEM), the transfer of model parameters between mountain ranges needs further investigation.

scholarly journals Creating New Near-Surface Air Temperature Datasets to Understand Elevation-Dependent Warming in the Tibetan Plateau

2020 ◽  
Vol 12 (11) ◽  
pp. 1722
Author(s):  
Mingxi Zhang ◽  
Bin Wang ◽  
James Cleverly ◽  
De Li Liu ◽  
Puyu Feng ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Air Temperature ◽  
Land Surface ◽  
Vegetation Indices ◽  
Surface Air Temperature ◽  
The Tibetan Plateau ◽  
Global Studies ◽  
Near Surface ◽  
Automated Mapping ◽  
Land Surface Temperatures

The Tibetan Plateau has been undergoing accelerated warming over recent decades, and is considered an indicator for broader global warming phenomena. However, our understanding of warming rates with elevation in complex mountain regions is incomplete. The most serious concern is the lack of high-quality near-surface air temperature (Tair) datasets in these areas. To address this knowledge gap, we developed an automated mapping framework for the estimation of seamless daily minimum and maximum Land Surface Temperatures (LSTs) for the Tibetan Plateau from the existing MODIS LST products for a long period of time (i.e., 2002–present). Specific machine learning methods were developed and linked with target-oriented validation and then applied to convert LST to Tair. Spatial variables in retrieving Tair, such as solar radiation and vegetation indices, were used in estimation of Tair, whereas MODIS LST products were mainly focused on temporal variation in surface air temperature. We validated our process using independent Tair products, revealing more reliable estimates on Tair; the R2 and RMSE at monthly scales generally fell in the range of 0.9–0.95 and 1–2 °C. Using these continuous and consistent Tair datasets, we found temperature increases in the elevation range between 2000–3000 m and 4000–5000 m, whereas the elevation interval at 6000–7000 m exhibits a cooling trend. The developed datasets, findings and methodology contribute to global studies on accelerated warming.

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