Multi-site evaluation of the JULES land surface model using global and local data

This study evaluates the ability of the JULES land surface model (LSM) to simulate photosynthesis using local and global data sets at 12 FLUXNET sites. Model parameters include site-specific (local) values for each flux tower site and the default parameters used in the Hadley Centre Global Environmental Model (HadGEM) climate model. Firstly, gross primary productivity (GPP) estimates from driving JULES with data derived from local site measurements were compared to observations from the FLUXNET network. When using local data, the model is biased with total annual GPP underestimated by 16% across all sites compared to observations. Secondly, GPP estimates from driving JULES with data derived from global parameter and atmospheric reanalysis (on scales of 100 km or so) were compared to FLUXNET observations. It was found that model performance decreases further, with total annual GPP underestimated by 30% across all sites compared to observations. When JULES was driven using local parameters and global meteorological data, it was shown that global data could be used in place of FLUXNET data with a 7% reduction in total annual simulated GPP. Thirdly, the global meteorological data sets, WFDEI and PRINCETON, were compared to local data to find that the WFDEI data set more closely matches the local meteorological measurements (FLUXNET). Finally, the JULES phenology model was tested by comparing results from simulations using the default phenology model to those forced with the remote sensing product MODIS leaf area index (LAI). Forcing the model with daily satellite LAI results in only small improvements in predicted GPP at a small number of sites, compared to using the default phenology model

Using IASI to simulate the total spectrum of outgoing long-wave radiances

A new method of deriving high-resolution top-of-atmosphere spectral radiances in 10 181 bands, over the whole outgoing long-wave spectrum of the Earth, is presented. Correlations between different channels measured by the Infrared Atmospheric Sounding Interfermeter (IASI) on the MetOp-A (Meteorological Operation) satellite and unobserved wavenumbers are used to estimate far infrared (FIR) radiances at 0.5 cm−1 intervals between 25.25 and 644.75 cm−1 (the FIR), and additionally between 2760 and 3000 cm−1 (the NIR – near infrared). Radiances simulated by the line-by-line radiative transfer model (LBLRTM) are used to construct the prediction model. The spectrum is validated by comparing the Integrated Nadir Long-wave Radiance (INLR) product spanning the whole 25.25–3000 cm−1 range with the corresponding broadband measurements from the Clouds and the Earth's Radiant Energy System (CERES) instrument on the Terra and Aqua satellites at points of simultaneous nadir overpass. There is a mean difference of 0.3 W m−2 sr−1 (0.5% relative difference). This is well within the uncertainties associated with the measurements made by either instrument. However, there is a noticeable contrast when the bias is separated into night-time and daytime scenes with the latter being significantly larger, possibly due to errors in the CERES Ed3 Spectral Response Functions (SRF) correction method. In the absence of an operational spaceborne instrument that isolates the FIR, this product provides a useful proxy for such measurements within the limits of the regression model it is based on, which is shown to have very low root mean squared errors. The new high-resolution spectrum is presented for global mean clear and all skies where the FIR is shown to contribute 44 and 47% to the total INLR, respectively. In terms of the spectral cloud effect (Cloud Integrated Nadir Long-wave Radiance – CINLR), the FIR contributes 19% and in some subtropical instances appears to be negative; results that would go unobserved with a traditional broadband analysis

Human influence on the record-breaking cold event in January of 2016 in Eastern China

Anthropogenic influences are estimated to have reduced the likelihood of an extreme cold event in midwinter with the intensity equal to or stronger than the record of 2016 in eastern China by about two‐thirds

Contribution of anthropogenic climate change to April-May 2017 heavy precipitation over the Uruguay River basin

Anthropogenic climate change has increased the risk of the April-May 2017 extreme rainfall in the Uruguay River basin, which has caused extensive flood and major socio-economic impacts, by at least twofold with a most-likely increase of about five