Status of the Global Observing System for Climate

Status of the Global Observing System for Climat

The ESA climate change initiative: Satellite data records for essential climate variables

The ESA’s Climate Change Initiative is reprocessing and reassessing over 40 years of multi-sensor satellite records to generate consistent, traceable, long-term datasets of “essential climate variables” for the climate modeling and research communities

Fire at high latitudes: Data-model comparisons and their consequences

Fire is an endemic process at high latitudes, connected to a range of other land surface properties, such as land cover, biomass, and permafrost, and intimately linked to the carbon balance of the high-latitude land surface. Much of our current understanding of these links and their climate consequences is through land surface models, so it is important to ensure that for their credibility, these models should be consistent with available data. Over the vast panboreal region, a key source of information on fire is satellite data. Comparisons between satellite-based burned area data from the Global Fire Emissions Database and three dynamic vegetation models (LPJ-WM, CLM4CN, and SDGVM) indicate that all models fail to represent the observed spatial and temporal properties of the fire regime. Although the three dynamic vegetation models give comparable values of the boreal net biome production (NBP), fire emissions are found to differ by a factor 4 between the models, because of widely different estimates of burned area and because of different parameterizations of the fuel load and combustion process. Including a more realistic representation of the fire regime in the models shows that for northern high latitudes, (i) severe fire years do not coincide with source years or vice versa, (ii) the interannual variability of fire emissions does not significantly affect the interannual variability of NBP, and (iii) overall biomass values alter only slightly, but the spatial distribution of biomass exhibits changes. We also demonstrate that it is crucial to alter the current representations of fire occurrence and severity in land surface models if the links between permafrost and fire are to be captured, in particular, the dynamics of permafrost properties, such as active layer depth. This is especially important if models are to be used to predict the effects of a changing climate, because of the consequences of permafrost changes for greenhouse gas emissions, hydrology, and land cover

A reassessment of temperature variations and trends from global reanalyses and monthly surface climatological datasets

The ERA-Interim and JRA-55 reanalyses of synoptic data and several conventional analyses of monthly climatological data provide similar estimates of global-mean surface warming since 1979. They broadly agree on the character of interannual variability and the extremity of the 2015/2016 warm spell to which a strong El Niño and low Arctic sea-ice cover contribute. Nevertheless global and regional averages differ on various time-scales due to differences in data coverage and sea-surface temperature analyses; averages from those conventional datasets that infill where they lack direct observations agree better with the averages from the reanalyses. The latest warm event is less extreme when viewed in terms of atmospheric energy, which gives more weight to variability in the Tropics, where the thermal signal has greater vertical penetration and latent energy is a larger factor. Surface warming from 1998 to 2012 is larger than indicated by earlier versions of the conventional datasets used to characterize what the Fifth Assessment Report of the Intergovernmental Panel on Climate Change termed a hiatus in global warming. None of the datasets exhibit net warming over the Antarctic since 1979. Centennial trends from the conventional datasets, HadCRUT4 on the one hand and GISTEMP and NOAAGlobalTemp on the other, differ mainly because sea-surface temperatures differ. Infilling of values where direct observations are lacking is more questionable for the data-sparse earlier decades. Change since the eighteenth century is inevitably more uncertain than change over and after a modern baseline period. The latter is arguably best estimated separately for taking stock of actions to limit climate change, exploiting reanalyses and using satellite data to refine the conventional approach. Nevertheless, early in 2016 the global temperature appears to have first touched or briefly breached a level 1.5 °C above that early in the Industrial Revolution, having touched the 1.0 °C level in 1998 during a previous El Niño

Effect of forest canopy structure on wintertime Land Surface Albedo: Evaluating CLM5 simulations with in-situ measurements

Land Surface Albedo (LSA) of forested environments continues to be a source of uncertainty in land surface modeling, especially across seasonally snow covered domains. Assessment and improvement of global scale model performance has been hampered by the contrasting spatial scales of model resolution and in-situ LSA measurements. In this study, point-scale simulations of the Community Land Model 5.0 (CLM5) were evaluated across a large range of forest structures and solar angles at two climatically different locations. LSA measurements, using an uncrewed aerial vehicle with up and down-looking shortwave radiation sensors, showed canopy structural shading of the snow surface exerted a primary control on LSA. Diurnal patterns of measured LSA revealed strong effects of both azimuth and zenith angles, neither of which were adequately represented in simulations. In sparse forest environments, LSA were overestimated by up to 66%. Further analysis revealed a lack of correlation between Plant Area Index (PAI), the primary canopy descriptor in CLM5, and measured LSA. Instead, measured LSA showed considerable correlation with the fraction of snow visible in the sensor's field of view, a correlation which increased further when only considering the sunlit fraction of visible snow. The use of effective PAI values as a simple first-order correction for the discrepancy between measured and simulated LSA in sparse forest environments substantially improved model results (64%–76% RMSE reduction). However, the large biases suggest the need for a more generic solution, for example, by introducing a canopy metric that represents canopy gap fraction rather than assuming a spatially homogeneous canopy

The European Space Agency BIOMASS mission: Measuring forest above-ground biomass from space

The primary objective of the European Space Agency's 7th Earth Explorer mission, BIOMASS, is to determine the worldwide distribution of forest above-ground biomass (AGB) in order to reduce the major uncertainties in calculations of carbon stocks and fluxes associated with the terrestrial biosphere, including carbon fluxes associated with Land Use Change, forest degradation and forest regrowth. To meet this objective it will carry, for the first time in space, a fully polarimetric P-band synthetic aperture radar (SAR). Three main products will be provided: global maps of both AGB and forest height, with a spatial resolution of 200 m, and maps of severe forest disturbance at 50 m resolution (where “global” is to be understood as subject to Space Object tracking radar restrictions). After launch in 2022, there will be a 3-month commissioning phase, followed by a 14-month phase during which there will be global coverage by SAR tomography. In the succeeding interferometric phase, global polarimetric interferometry Pol-InSAR coverage will be achieved every 7 months up to the end of the 5-year mission. Both Pol-InSAR and TomoSAR will be used to eliminate scattering from the ground (both direct and double bounce backscatter) in forests. In dense tropical forests AGB can then be estimated from the remaining volume scattering using non-linear inversion of a backscattering model. Airborne campaigns in the tropics also indicate that AGB is highly correlated with the backscatter from around 30 m above the ground, as measured by tomography. In contrast, double bounce scattering appears to carry important information about the AGB of boreal forests, so ground cancellation may not be appropriate and the best approach for such forests remains to be finalized. Several methods to exploit these new data in carbon cycle calculations have already been demonstrated. In addition, major mutual gains will be made by combining BIOMASS data with data from other missions that will measure forest biomass, structure, height and change, including the NASA Global Ecosystem Dynamics Investigation lidar deployed on the International Space Station after its launch in December 2018, and the NASA-ISRO NISAR L- and S-band SAR, due for launch in 2022. More generally, space-based measurements of biomass are a core component of a carbon cycle observation and modelling strategy developed by the Group on Earth Observations. Secondary objectives of the mission include imaging of sub-surface geological structures in arid environments, generation of a true Digital Terrain Model without biases caused by forest cover, and measurement of glacier and icesheet velocities. In addition, the operations needed for ionospheric correction of the data will allow very sensitive estimates of ionospheric Total Electron Content and its changes along the dawn-dusk orbit of the mission

Validating canopy clumping retrieval methods using hemispherical photography in a simulated Eucalypt forest

The so-called clumping factor (Ω) quantifies deviation from a random 3D distribution of material in a vegetation canopy and therefore characterises the spatial distribution of gaps within a canopy. Ω is essential to convert effective Plant or Leaf Area Index into actual LAI or PAI, which has previously been shown to have a significant impact on biophysical parameter retrieval using optical remote sensing techniques in forests, woodlands, and savannas. Here, a simulation framework was applied to assess the performance of existing in situ clumping retrieval methods in a 3D virtual forest canopy, which has a high degree of architectural realism. The virtual canopy was reconstructed using empirical data from a Box Ironbark Eucalypt forest in Eastern Australia. Hemispherical photography (HP) was assessed due to its ubiquity for indirect LAI and structure retrieval. Angular clumping retrieval method performance was evaluated using a range of structural configurations based on varying stem distribution and LAI. The CLX clumping retrieval method (Leblanc et al., 2005) with a segment size of 15° was the best performing clumping method, matching the reference values to within 0.05 Ω on average near zenith. Clumping error increased linearly with zenith angle to > 0.3 Ω (equivalent to a 30% PAI error) at 75° for all structural configurations. At larger zenith angles, PAI errors were found to be around 25–30% on average when derived from the 55–60° zenith angle. Therefore, careful consideration of zenith angle range utilised from HP is recommended. We suggest that plot or site clumping factors should be accompanied by the zenith angle used to derive them from gap size and gap size distribution methods. Furthermore, larger errors and biases were found for HPs captured within 1 m of unrepresentative large tree stems, so these situations should be avoided in practice if possible

Evaluating operational AVHRR sea surface temperature data at the coastline using surfers

Sea surface temperature (SST) is an essential climate variable that can be measured routinely from Earth Observation (EO) with high temporal and spatial coverage. To evaluate its suitability for an application, it is critical to know the accuracy and precision (performance) of the EO SST data. This requires comparisons with co-located and concomitant in situ data. Owing to a relatively large network of in situ platforms there is a good understanding of the performance of EO SST data in the open ocean. However, at the coastline this performance is not well known, impeded by a lack of in situ data. Here, we used in situ SST measurements collected by a group of surfers over a three year period in the coastal waters of the UK and Ireland, to improve our understanding of the performance of EO SST data at the coastline. At two beaches near the city of Plymouth, UK, the in situ SST measurements collected by the surfers were compared with in situ SST collected from two autonomous buoys located ∼7 km and ∼33 km from the coastline, and showed good agreement, with discrepancies consistent with the spatial separation of the sites. The in situ SST measurements collected by the surfers around the coastline, and those collected offshore by the two autonomous buoys, were used to evaluate the performance of operational Advanced Very High Resolution Radiometer (AVHRR) EO SST data. Results indicate: (i) a significant reduction in the performance of AVHRR at retrieving SST at the coastline, with root mean square errors in the range of 1.0 to 2.0 °C depending on the temporal difference between match-ups, significantly higher than those at the two offshore stations (0.4 to 0.6 °C); (ii) a systematic negative bias in the AVHRR retrievals of approximately 1 °C at the coastline, not observed at the two offshore stations; and (iii) an increase in the root mean square error at the coastline when the temporal difference between match-ups exceeded three hours. Harnessing new solutions to improve in situ sampling coverage at the coastline, such as tagging surfers with sensors, can improve our understanding of the performance of EO SST data in coastal regions, helping inform users interested in EO SST products for coastal applications. Yet, validating EO SST products using in situ SST data at the coastline is challenged by difficulties reconciling the two measurements, which are provided at different spatial scales in a dynamic and complex environment

Challenges in using land use and land cover data for global change studies

Land use and land cover data play a central role in climate change assessments. These data originate from different sources and inventory techniques. Each source of land use/cover data has its own domain of applicability and quality standards. Often data are selected without explicitly considering the suitability of the data for the specific application, the bias originating from data inventory and aggregation, and the effects of the uncertainty in the data on the results of the assessment. Uncertainties due to data selection and handling can be in the same order of magnitude as uncertainties related to the representation of the processes under investigation. While acknowledging the differences in data sources and the causes of inconsistencies, several methods have been developed to optimally extract information from the data and document the uncertainties. These methods include data integration, improved validation techniques and harmonization of classification systems. Based on the data needs of global change studies and the data availability, recommendations are formulated aimed at optimal use of current data and focused efforts for additional data collection. These include: improved documentation using classification systems for land use/cover data; careful selection of data given the specific application and the use of appropriate scaling and aggregation methods. In addition, the data availability may be improved by the combination of different data sources to optimize information content while collection of additional data must focus on validation of available data sets and improved coverage of regions and land cover types with a high level of uncertainty. Specific attention in data collection should be given to the representation of land management (systems) and mosaic landscape