A Revisit of Global Dimming and Brightening Based on the Sunshine Duration

Frassl

et al. 2020

Water

Self Cite

Lake surface water temperature (LSWT) plays a fundamental role in the lake energy budget. However, direct observations of LSWT require considerable effort for acquisition and hence are rare relative to a large number of lakes. In lakes where LSWT has not been covered sufficiently by in situ measurements, remote sensing and lake modeling can be used to produce a fine spatio-temporal record of LSWTs. In our study, the Moderate-Resolution Imaging Spectroradiometer (MODIS) LSWT was used to compare with in situ data at the overpass times over the six sites in Lake Chaohu, a large shallow lake in China. MODIS-derived LSWT reflected the variation of lake surface temperature well, with a correlation coefficient of 0.96 and a cool bias of 1.25 °C. The bias was modified by an “Upper Envelop” smoothing method and then employed to evaluate the general lake model (GLM) performance, a one-dimensional hydrodynamic model. The GLM simulations showed good performance compared with MODIS LSWT data at an interannual time scale. A 57-year record of simulated LSWT was hindcast by the well-calibrated GLM for Lake Chaohu. The results showed that LSWT decreased by 0.08 °C/year from 1960 to 1981 and then increased by 0.05 °C/year. These trends were most likely caused by a cooling effect of decreased surface incident solar radiation and a warming effect of reduced wind speed. Our study promoted the use of MODIS-derived LSWT as an alternative data source, and then combined with a numerical model for inland water surface temperature, and also further provided an understanding of climate warming effect on such a shallow eutrophic lake. Key points: (1) Moderate-Resolution Imaging Spectroradiometer (MODIS) lake water surface temperature (LSWT) was validated with real-time in situ data collected at Lake Chaohu with high accuracy; (2) MODIS LSWT was modified by the bias correction and employed to evaluate a one-dimensional lake model at interannual and intraannual scale; The LSWT hindcast by a well-calibrated model at Lake Chaohu decreased by 0.08 °C/year from 1960 to 1981 and increased by 0.05 °C/year from 1982 to 2016.

Section: Evaluation Of Modis Lake Surface Water Temperature (Lswt) Wisupporting

confidence: 59%

Section: Discussionmentioning

confidence: 97%

Section: Evaluation Of Modis Lake Surface Water Temperature (Lswt) Wimentioning

confidence: 99%

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Reconstructing Six Decades of Surface Temperatures at a Shallow Lake

Zhang

Frassl

et al. 2020

Water

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“…The equations of R dir and R dif are regressed based on the observed direct and diffuse solar radiation from 16 CMA stations during the period 1994–2014. Analogously, the calculation formula for the monthly derived R dir data set is improved based on that for the derived R s data set (He et al, ; Tang et al, ; Yang et al, ), as shown in equation :

R_{dir} / R_{c_italicdir} = a_{0} + a_{1} \times ()(, n / N) + a_{2} \times {(n / N)}^{2}

R_{c_italicdir} = \int τ_{c_italicdir} \times I_{0} italicdt

…”

Section: Methodsmentioning

confidence: 99%

Variability in Direct and Diffuse Solar Radiation Across China From 1958 to 2017

Geophysical Research Letters

2020

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Long‐term variability of direct and diffuse solar radiation (Rdir and Rdif) is essential for climate change study. However, Rdir and Rdif observations suffer from low spatiotemporal coverage and inhomogeneity. This study improved hybrid models to calculate Rdir and Rdif from sunshine duration and meteorological data at ~2,000 stations from 1958 to 2017 over China and demonstrated their reliability. We identified that Rdir observations show a spurious steep downtrend before 1990 due to the sensitivity drift of the measuring instruments, implying an overestimation of global dimming. Long‐term trends and spatiotemporal details in Rdir and Rdif were also revealed. From 1958 to 1989, our results show that Rdir displays a significant downtrend (−3.52 W m−2 per decade), whereas Rdif shows a significant increasing trend (0.84 W m−2 per decade), especially over the North China Plain. From 1990 to 2017, Rdir decreases nonsignificantly by −0.47 W m−2 per decade but Rdif shows a slight decline of −0.28 W m−2 per decade.

“…Moreover, the solar radiation measurements over China suffer from problems such as sensitivity drift and instrument replacement (Wang, ; Wang et al, ; Yang et al, ; Zhang & Lu, , ). Sunshine duration (SunDu) records have been suggested that can be used to reconstruct long‐term R s with comparatively large spatial coverage (He et al, ; Matuszko, ; Sanchez‐Lorenzo et al, ; Wang et al, b; Yang et al, ). Satellite‐derived R s products also provide an estimate of R s (Feng & Wang, ; Li et al, ; Ma et al, ).…”

Section: Introductionmentioning

confidence: 99%

Determining Factors of Monthly to Decadal Variability in Surface Solar Radiation in China: Evidences From Current Reanalyses

Feng

2019

JGR Atmospheres

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Clouds and aerosols play essential roles in regulating surface incident solar radiation (Rs). It has been suggested that the increased aerosol loading over China is a key factor for the decadal variability in Rs and can explain the bias in its trend from reanalyses because the reanalyses do not include the interannual variability of aerosols. In this study, we compare the Rs derived from sunshine duration at 2,400 weather stations in China and that from five reanalyses from 1980 to 2014. The determining factors for the biases in the mean values and trends of Rs from the reanalyses are examined, with the help of Rs and the cloud fraction (CF), from satellite and 2,400 weather stations. Our results show that all reanalyses overestimate the multiyear Rs by 24.10–40.00 W/m2 due to their underestimations of CF, which is more obvious in southern China. The biases in the simulated CF in the reanalyses can explain the biases in Rs by 55–41%, and the bias in clear‐sky surface solar radiation (Rc), which is primarily due to biases in aerosol loading, can explain 32–9% of the bias in Rs. The errors in the trend of the simulated CF can explain the errors in the Rs trends in the reanalyses by 73–12%, and the trend errors in the Rc can explain 43–30% of the trend error in Rs. Our study suggests that more work is needed to improve the simulation of aerosols, clouds, and aerosol‐cloud‐radiation interactions in the reanalyses.