High-resolution spatiotemporal modeling of daily near-surface air temperature in Germany over the period 2000-2020

The commonly used weather stations cannot fully capture the spatiotemporal variability of near-surface air temperature (T_air), leading to exposure misclassification and biased health effect estimates. We aimed to improve the spatiotemporal coverage of T_air data in Germany by using multi-stage modeling to estimate daily 1 × 1 km minimum (T_min), mean (T_mean), maximum (T_max) T_air and diurnal T_air range during 2000-2020. We used weather station T_air observations, satellite-based land surface temperature (LST), elevation, vegetation and various land use predictors. In the first stage, we built a linear mixed model with daily random intercepts and slopes for LST adjusted for several spatial predictors to estimate T_air from cells with both T_air and LST available. In the second stage, we used this model to predict T_air for cells with only LST available. In the third stage, we regressed the second stage predictions against interpolated T_air values to obtain T_air countrywide. All models achieved high accuracy (0.91 ≤ R² ≤ 0.98) and low errors (1.03 °C ≤ Root Mean Square Error (RMSE) ≤ 2.02 °C). Validation with external data confirmed the good performance, locally, i.e., in Augsburg for all models (0.74 ≤ R² ≤ 0.99, 0.87 °C ≤ RMSE ≤ 2.05 °C) and countrywide, for the T_mean model (0.71 ≤ R² ≤ 0.99, 0.79 °C ≤ RMSE ≤ 1.19 °C). Annual T_mean averages ranged from 8.56 °C to 10.42 °C with the years beyond 2016 being constantly hotter than the 21-year average. The spatial variability within Germany exceeded 15 °C annually on average following patterns including mountains, rivers and urbanization. Using a case study, we showed that modeling leads to broader T_air variability representation for exposure assessment of participants in health cohorts. Our results indicate the proposed models as suitable for estimating nationwide T_air at high resolution. Our product is critical for temperature-based epidemiological studies and is also available for other research purposes.

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.

Contrast patterns and trends of lapse rates calculated from near-surface air and land surface temperatures in China from 1961 to 2014

The near-surface lapse rate reflects the atmospheric stability above the surface. Lapse rates calculated from land surface temperature (γ_Ts) and near-surface air temperature (γ_Ta) have been widely used. However, γ_Ts and γ_Ta have different sensitivity to local surface energy balance and large-scale energy transport and therefore they may have diverse spatial and temporal variability, which has not been clearly illustrated in existing studies. In this study, we calculated and compared γ_Ta and γ_Ts at ~ 2200 stations over China from 1961 to 2014. This study finds that γ_Ta and γ_Ts have a similar multiyear national average (0.53 °C/100 m) and seasonal cycle. Nevertheless, γ_Ts shows steeper multiyear average than γ_Ta at high latitudes, and γ_Ts in summer is steeper than γ_Ta, especially in Northwest China. The North China shows the shallowest γ_Ta and γ_Ts, then inhibiting the vertical diffusion of air pollutants and further reducing the lapse rates due to accumulation of pollutants. Moreover, the long-term trend signs for γ_Ta and γ_Ts are opposite in northern China. However, the trends in γ_Ta and γ_Ts are both negative in Southwest China and positive in Southeast China. Surface incident solar radiation, surface downward longwave radiation and precipitant frequency jointly can account for 80% and 75% of the long-term trends in γ_Ta and γ_Ts in China, respectively, which provides an explanation of trends of γ_Ta and γ_Ts from perspective of surface energy balance.