Recovering land surface temperature under cloudy skies considering the solar‐cloud‐satellite geometry: application to MODIS and Landsat‐8 data

Clouds play a significant role in the derivation of land surface temperature (LST) from optical remote sensing. The estimation of LST under cloudy sky conditions has been a great challenge for the community for a long time. In this study, a scheme for recovering the LST under cloudy skies is proposed by accounting for the solar‐cloud‐satellite geometry effect, through which the LSTs of shadowed and illuminated pixels covered by clouds in the image are estimated. The validation shows that the new scheme can work well and has reasonable LST accuracy with a root mean square error < 4.9 K and bias < 3.5 K. The application of the new method to the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat‐8 data reveals that the LSTs under cloud layers can be reasonably recovered and that the fraction of valid LSTs in an image can be correspondingly improved. The method is not data specific; instead, it can be used in any optical remote sensing images as long as the proper input variables are provided. As an alternative approach to derive cloudy sky LSTs based only on optical remote sensing data, it gives some new ideas to the remote sensing community, especially in the fields of surface energy balance

Increased importance of methane reduction for a 1.5 degree target

To understand the importance of methane on the levels of carbon emission reductions required to achieve temperature goals, a processed-based approach is necessary rather than reliance on the Transient Climate Response to Emissions. We show that plausible levels of methane (CH4) mitigation can make a substantial difference to the feasibility of achieving the Paris climate targets through increasing the allowable carbon emissions. This benefit is enhanced by the indirect effects of CH4 on ozone (O3). Here the differing effects of CH4 and CO2 on land carbon storage, including the effects of surface O3, lead to an additional increase in the allowable carbon emissions with CH4 mitigation. We find a simple robust relationship between the change in the 2100 CH4 concentration and the extra allowable cumulative carbon emissions between now and 2100 (0.27 ± 0.05 GtC per ppb CH4). This relationship is independent of modelled climate sensitivity and precise temperature target, although later mitigation of CH4 reduces its value and thus methane reduction effectiveness. Up to 12% of this increase in allowable emissions is due to the effect of surface ozone. We conclude early mitigation of CH4 emissions would significantly increase the feasibility of stabilising global warming below 1.5C, alongside having co-benefits for human and ecosystem health

Comparison of greenhouse gas fluxes from tropical forests and oil palm plantations on mineral soil

In Southeast Asia, oil palm (OP) plantations have largely replaced tropical forests. The impact of this shift in land use on greenhouse gas (GHG) fluxes remains highly uncertain, mainly due to a relatively small pool of available data. The aim of this study is to quantify differences of nitrous oxide (N2O) and methane (CH4) fluxes as well as soil carbon dioxide (CO2) respiration rates from logged forests, oil palm plantations of different ages, and an adjacent small riparian area. Nitrous oxide fluxes are the focus of this study, as these emissions are expected to increase significantly due to the nitrogen (N) fertilizer application in the plantations. This study was conducted in the SAFE (Stability of Altered Forest Ecosystems) landscape in Malaysian Borneo (Sabah) with measurements every 2 months over a 2-year period. GHG fluxes were measured by static chambers together with key soil physicochemical parameters and microbial biodiversity. At all sites, N2O fluxes were spatially and temporally highly variable. On average the largest fluxes (incl. 95 % CI) were measured from OP plantations (45.1 (24.0–78.5) µg m−2 h−1 N2O-N), slightly smaller fluxes from the riparian area (29.4 (2.8–84.7) µg m−2 h−1 N2O-N), and the smallest fluxes from logged forests (16.0 (4.0–36.3) µg m−2 h−1 N2O-N). Methane fluxes were generally small (mean ± SD): −2.6 ± 17.2 µg CH4-C m−2 h−1 for OP and 1.3 ± 12.6 µg CH4-C m−2 h−1 for riparian, with the range of measured CH4 fluxes being largest in logged forests (2.2 ± 48.3 µg CH4-C m−2 h−1). Soil respiration rates were larger from riparian areas (157.7 ± 106 mg m−2 h−1 CO2-C) and logged forests (137.4 ± 95 mg m−2 h−1 CO2-C) than OP plantations (93.3 ± 70 mg m−2 h−1 CO2-C) as a result of larger amounts of decomposing leaf litter. Microbial communities were distinctly different between the different land-use types and sites. Bacterial communities were linked to soil pH, and fungal and eukaryotic communities were linked to land use. Despite measuring a large number of environmental parameters, mixed models could only explain up to 17 % of the variance of measured fluxes for N2O, 3 % of CH4, and 25 % of soil respiration. Scaling up measured N2O fluxes to Sabah using land areas for forest and OP resulted in emissions increasing from 7.6 Mt (95 % confidence interval, −3.0–22.3 Mt) yr−1 in 1973 to 11.4 Mt (0.2–28.6 Mt) yr−1 in 2015 due to the increasing area of forest converted to OP plantations over the last ∼ 40 years

Investigation of the summer 2018 European ozone air pollution episodes using novel satellite data and modelling

In the summer of 2018, Europe experienced an intense heat wave which coincided with several persistent large-scale ozone (O3) pollution episodes. Novel satellite data of lower tropospheric column O3 from the Global Ozone Monitoring Experiment-2 (GOME-2) and Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp satellite showed substantial enhancements in 2018 relative to other years since 2012. Surface observations also showed ozone enhancements across large regions of continental Europe in summer 2018 compared to 2017. Enhancements to surface temperature and the O3 precursor gases carbon monoxide and methanol in 2018 were co-retrieved from MetOp observations by the same scheme. This analysis was supported by the TOMCAT chemistry transport model (CTM) to investigate processes driving the observed O3 enhancements. Through several targeted sensitivity experiments we show that meteorological processes, and emissions to a secondary order, were important for controlling the elevated O3 concentrations at the surface. However, mid-tropospheric (~500 hPa) O3 enhancements were dominated by meteorological processes. We find that contributions from stratospheric O3 intrusions ranged between 15&ndash;40 %. Analysis of back trajectories indicates that the import of O3-enriched air masses into Europe originated over the North Atlantic substantially increasing O3 in the 500 hPa layer during summer 2018.</p

Evaluation of wetland CH4 in the Joint UK Land Environment Simulator (JULES) land surface model using satellite observations

Wetlands are the largest natural source of methane. The ability to model the emissions of methane from natural wetlands accurately is critical to our understanding of the global methane budget and how it may change under future climate scenarios. The simulation of wetland methane emissions involves a complicated system of meteorological drivers coupled to hydrological and biogeochemical processes. The Joint UK Land Environment Simulator (JULES) is a process-based land surface model that underpins the UK Earth System Model (UKESM) and is capable of generating estimates of wetland methane emissions. In this study, we use GOSAT satellite observations of atmospheric methane along with the TOMCAT global 3-D chemistry transport model to evaluate the performance of JULES in reproducing the seasonal cycle of methane over a wide range of tropical wetlands. By using an ensemble of JULES simulations with differing input data and process configurations, we investigate the relative importance of the meteorological driving data, the vegetation, the temperature dependency of wetland methane production and the wetland extent. We find that JULES typically performs well in replicating the observed methane seasonal cycle. We calculate correlation coefficients to the observed seasonal cycle of between 0.58 and 0.88 for most regions; however, the seasonal cycle amplitude is typically underestimated (by between 1.8 and 19.5 ppb). This level of performance is comparable to that typically provided by state-of-the-art data-driven wetland CH4 emission inventories. The meteorological driving data are found to be the most significant factor in determining the ensemble performance, with temperature dependency and vegetation having moderate effects. We find that neither wetland extent configuration outperforms the other, but this does lead to poor performance in some regions. We focus in detail on three African wetland regions (Sudd, Southern Africa and Congo) where we find the performance of JULES to be poor and explore the reasons for this in detail. We find that neither wetland extent configuration used is sufficient in representing the wetland distribution in these regions (underestimating the wetland seasonal cycle amplitude by 11.1, 19.5 and 10.1 ppb respectively, with correlation coefficients of 0.23, 0.01 and 0.31). We employ the Catchment-based Macro-scale Floodplain (CaMa-Flood) model to explicitly represent river and floodplain water dynamics and find that these JULES-CaMa-Flood simulations are capable of providing a wetland extent that is more consistent with observations in this regions, highlighting this as an important area for future model development.</p

Quantifying the UK's carbon dioxide flux: An atmospheric inverse modelling approach using a regional measurement network

We present a method to derive atmosphericobservation-based estimates of carbon dioxide (CO 2 ) fluxes at the national scale, demonstrated using data from a network of surface tall-tower sites across the UK and Ireland over the period 2013-2014. The inversion is carried out using simulations from a Lagrangian chemical transport model and an innovative hierarchical Bayesian Markov chain Monte Carlo (MCMC) framework, which addresses some of the traditional problems faced by inverse modelling studies, such as subjectivity in the specification of model and prior uncertainties. Biospheric fluxes related to gross primary productivity and terrestrial ecosystem respiration are solved separately in the inversion and then combined a posteriori to determine net ecosystem exchange of CO 2 . Two different models, Data Assimilation Linked Ecosystem Carbon (DALEC) and Joint UK Land Environment Simulator (JULES), provide prior estimates for these fluxes. We carry out separate inversions to assess the impact of these different priors on the posterior flux estimates and evaluate the differences between the prior and posterior estimates in terms of missing model components. The Numerical Atmospheric dispersion Modelling Environment (NAME) is used to relate fluxes to the measurements taken across the regional network. Posterior CO2 estimates from the two inversions agree within estimated uncertainties, despite large differences in the prior fluxes from the different models. With our method, averaging results from 2013 and 2014, we find a total annual net biospheric flux for the UK of 8±79 TgCO 2 yr -1 (DALEC prior) and 64±85 TgCO 2 yr -1 (JULES prior), where negative values represent an uptake of CO 2 . These biospheric CO 2 estimates show that annual UK biospheric sources and sinks are roughly in balance. These annual mean estimates consistently indicate a greater net release of CO 2 than the prior estimates, which show much more pronounced uptake in summer months

Regional variation in the effectiveness of methane-based and land-based climate mitigation options

Scenarios avoiding global warming greater than 1.5 or 2°C, as stipulated in the Paris Agreement, may require the combined mitigation of anthropogenic greenhouse gas emissions alongside enhancing negative emissions through approaches such as afforestation/reforestation (AR) and biomass energy with carbon capture and storage (BECCS). We use the JULES land-surface model coupled to an inverted form of the IMOGEN climate emulator to investigate mitigation scenarios that achieve the 1.5 or 2°C warming targets of the Paris Agreement. Specifically, within this IMOGEN-JULES framework, we focus on and characterise the global and regional effectiveness of land-based (BECCS and/or AR) and anthropogenic methane (CH4) emission mitigation, separately and in combination, on the anthropogenic fossil fuel carbon dioxide (CO2) emission budgets (AFFEBs) to 2100. We use consistent data and socio-economic assumptions from the IMAGE integrated assessment model for the second Shared Socioeconomic Pathway (SSP2). The analysis includes the effects of the methane and carbon-climate feedbacks from wetlands and permafrost thaw, which we have shown previously to be significant constraints on the AFFEBs. Globally, mitigation of anthropogenic CH4 emissions has large impacts on the anthropogenic fossil fuel emission budgets, potentially offsetting (i.e. allowing extra) carbon dioxide emissions of 188-212 GtC. This is because of (a) the reduction in the direct and indirect radiative forcing of methane in response to the lower emissions and hence atmospheric concentration of methane; and (b) carbon-cycle changes leading to increased uptake by the land and ocean by CO2-based fertilisation. Methane mitigation is beneficial everywhere, particularly for the major CH4-emitting regions of India, USA and China. Land-based mitigation has the potential to offset 51-100 GtC globally, the large range reflecting assumptions and uncertainties associated with BECCS. The ranges for CH4 reduction and BECCs implementation are valid for both the 1.5° and 2°C warming targets. 2 That is the mitigation potential of the CH4 and of the land-based scenarios is similar for whether society aims for one or other 35 of the final stabilised warming levels. Further, both the effectiveness and the preferred land-management strategy (i.e., AR or BECCS) have strong regional dependencies. Additional analysis shows extensive BECCS could adversely affect water security for several regions. Although the primary requirement remains mitigation of fossil fuel emissions, our results highlight the potential for the mitigation of CH4 emissions to make the Paris climate targets more achievabl

Evaluation of wetland CH4 in the Joint UK Land Environment Simulator (JULES) land surface model using satellite observations

Wetlands are the largest natural source of methane. The ability to model the emissions of methane from natural wetlands accurately is critical to our understanding of the global methane budget and how it may change under future climate scenarios. The simulation of wetland methane emissions involves a complicated system of meteorological drivers coupled to hydrological and biogeochemical processes. The Joint UK Land Environment Simulator (JULES) is a process-based land surface model that underpins the UK Earth System Model (UKESM) and is capable of generating estimates of wetland methane emissions. In this study, we use GOSAT satellite observations of atmospheric methane along with the TOMCAT global 3-D chemistry transport model to evaluate the performance of JULES in reproducing the seasonal cycle of methane over a wide range of tropical wetlands. By using an ensemble of JULES simulations with differing input data and process configurations, we investigate the relative importance of the meteorological driving data, the vegetation, the temperature dependency of wetland methane production and the wetland extent. We find that JULES typically performs well in replicating the observed methane seasonal cycle. We calculate correlation coefficients to the observed seasonal cycle of between 0.58 and 0.88 for most regions; however, the seasonal cycle amplitude is typically underestimated (by between 1.8 and 19.5 ppb). This level of performance is comparable to that typically provided by state-of-the-art data-driven wetland CH4 emission inventories. The meteorological driving data are found to be the most significant factor in determining the ensemble performance, with temperature dependency and vegetation having moderate effects. We find that neither wetland extent configuration outperforms the other, but this does lead to poor performance in some regions. We focus in detail on three African wetland regions (Sudd, Southern Africa and Congo) where we find the performance of JULES to be poor and explore the reasons for this in detail. We find that neither wetland extent configuration used is sufficient in representing the wetland distribution in these regions (underestimating the wetland seasonal cycle amplitude by 11.1, 19.5 and 10.1 ppb respectively, with correlation coefficients of 0.23, 0.01 and 0.31). We employ the Catchment-based Macro-scale Floodplain (CaMa-Flood) model to explicitly represent river and floodplain water dynamics and find that these JULES-CaMa-Flood simulations are capable of providing a wetland extent that is more consistent with observations in this regions, highlighting this as an important area for future model development

Modeled microbial dynamics explain the apparent temperature sensitivity of wetland methane emissions

Methane emissions from natural wetlands tend to increase with temperature and therefore may lead to a positive feedback under future climate change. However, their temperature response includes confounding factors and appears to differ on different time scales. Observed methane emissions depend strongly on temperature on a seasonal basis, but if the annual mean emissions are compared between sites, there is only a small temperature effect. We hypothesize that microbial dynamics are a major driver of the seasonal cycle and that they can explain this apparent discrepancy. We introduce a relatively simple model of methanogenic growth and dormancy into a wetland methane scheme that is used in an Earth system model. We show that this addition is sufficient to reproduce the observed seasonal dynamics of methane emissions in fully saturated wetland sites, at the same time as reproducing the annual mean emissions. We find that a more complex scheme used in recent Earth system models does not add predictive power. The sites used span a range of climatic conditions, with the majority in high latitudes. The difference in apparent temperature sensitivity seasonally versus spatially cannot be recreated by the non‐microbial schemes tested. We therefore conclude that microbial dynamics are a strong candidate to be driving the seasonal cycle of wetland methane emissions. We quantify longer‐term temperature sensitivity using this scheme and show that it gives approximately a 12% increase in emissions per degree of warming globally. This is in addition to any hydrological changes, which could also impact future methane emissions

Carbon budget for 1.5 and 2oC targets lowered by natural wetland and permafrost feedbacks

Methane emissions from natural wetlands and carbon release from permafrost thaw have a positive feedback on climate, yet are not represented in most state-of-the-art climate models. Furthermore, a fraction of the thawed permafrost carbon is released as methane, enhancing the combined feedback strength. We present simulations with an intermediate complexity climate model which follow prescribed global warming pathways to stabilisation at 1.5°C or 2.0°C above pre-industrial levels by the year 2100, and that incorporates a state-of-the-art global land surface model with updated descriptions of wetland and permafrost carbon release. We demonstrate that the climate feedbacks from those two processes are substantial. Specifically, permissible anthropogenic fossil fuel CO2 emission budgets are reduced by 17-23% (47-56 GtC) for stabilisation at 1.5°C, and 9-13% (52-57 GtC) for 2.0°C stabilisation. In our simulations these feedback processes respond faster at temperatures below 1.5°C, and the differences between the 1.5°C and 2°C targets are disproportionately small. This key finding is due to our interest in transient emission pathways to the year 2100 and does not consider the longer term implications of these feedback processes. We conclude that natural feedback processes from wetlands and permafrost must be considered in assessments of transient emission pathways to limit global warming