Remote Sensing Evaluation of CLM4 GPP for the Period 2000-2009*

Remote sensing can provide long-term and large-scale products helpful for ecosystem model evaluation. The authors compare monthly gross primary production (GPP) simulated by the Community Land Model, version 4 (CLM4) at a half-degree resolution with satellite estimates of GPP from the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (MOD17) for the 10-yr period January 2000–December 2009. The assessment is presented in terms of long-term mean carbon assimilation, seasonal mean distributions, amplitude and phase of the annual cycle, and intraannual and interannual GPP variability and their responses to climate variables. For the long-term annual and seasonal means, major GPP patterns are clearly demonstrated by both products. Compared to the MODIS product, CLM4 overestimates the magnitude of GPP for tropical evergreen forests. CLM4 has a longer carbon uptake period than MODIS for most plant functional types (PFTs) with an earlier onset of GPP in spring and a later decline of GPP in autumn. Empirical orthogonal function analysis of the monthly GPP changes indicates that, on the intraannual scale, both CLM4 and MODIS display similar spatial representations and temporal patterns for most terrestrial ecosystems except in northeast Russia and in the very dry region of central Australia. For 2000–09, CLM4 simulated increases in annual averaged GPP over both hemispheres; however, estimates from MODIS suggest a reduction in the Southern Hemisphere (−0.2173 PgC yr−1), balancing the significant increase over the Northern Hemisphere (0.2157 PgC yr−1). The evaluations highlight strengths and weaknesses of the CLM4 primary production and illuminate potential improvements and developments

A continental phenology model for monitoring vegetation responses to interannual climatic variability

Regional phenology is important in ecosystem simulation models and coupled biosphere/atmosphere models. In the continental United States, the timing of the onset of greenness in the spring (leaf expansion, grass green-up) and offset of greenness in the fall (leaf abscission, cessation of height growth, grass brown-off) are strongly influenced by meteorological and climatological conditions. We developed predictive phenology models based on traditional phenology research using commonly available meteorological and climatological data. Predictions were compared with satellite phenology observations at numerous 20 km × 20 km contiguous landcover sites. Onset mean absolute error was 7.2 days in the deciduous broadleaf forest (DBF) biome and 6.1 days in the grassland biome. Offset mean absolute error was 5.3 days in the DBF biome and 6.3 days in the grassland biome. Maximum expected errors at a 95% probability level ranged from 10 to 14 days. Onset was strongly associated with temperature summations in both grassland and DBF biomes; DBF offset was best predicted with a photoperiod function, while grassland offset required a combination of precipitation and temperature controls. A long-term regional test of the DBF onset model captured field-measured interannual variability trends in lilac phenology. Continental application of the phenology models for 1990–1992 revealed extensive interannual variability in onset and offset. Median continental growing season length ranged from a low of 129 days in 1991 to a high of 146 days in 1992. Potential uses of the models include regulation of the timing and length of the growing season in large-scale biogeochemical models and monitoring vegetation response to interannual climatic variability

Parameterization and Sensitivity Analysis of the BIOME-BGC Terrestrial Ecosystem model: Net Primary Production Controls

Ecosystem simulation models use descriptive input parameters to establish the physiology, biochemistry, structure, and allocation patterns of vegetation functional types, or biomes. For single-stand simulations it is possible to measure required data, but as spatial resolution increases, so too does data unavailability. Generalized biome parameterizations are then required. Undocumented parameter selection and unknown model sensitivity to parameter variation for larger-resolution simulations are currently the major limitations to global and regional modeling. The authors present documented input parameters for a process-based ecosystem simulation model, BIOME–BGC, for major natural temperate biomes. Parameter groups include the following: turnover and mortality; allocation; carbon to nitrogen ratios (C:N); the percent of plant material in labile, cellulose, and lignin pools; leaf morphology; leaf conductance rates and limitations; canopy water interception and light extinction; and the percent of leaf nitrogen in Rubisco (ribulose bisphosphate-1,5-carboxylase/oxygenase) (PLNR). Using climatic and site description data from the Vegetation/Ecosystem Modeling and Analysis Project, the sensitivity of predicted annual net primary production (NPP) to variations in parameter level of ± 20% of the mean value was tested. For parameters exhibiting a strong control on NPP, a factorial analysis was conducted to test for interaction effects. All biomes were affected by variation in leaf and fine root C:N. Woody biomes were additionally strongly controlled by PLNR, maximum stomatal conductance, and specific leaf area while nonwoody biomes were sensitive to fire mortality and litter quality. None of the critical parameters demonstrated strong interaction effects. An alternative parameterization scheme is presented to better represent the spatial variability in several of these critical parameters. Patterns of general ecological function drawn from the sensitivity analysis are discussed

Assessing simulation ecosystem processes for climate variability research at Glacier National Park

Glacier National Park served as a test site for ecosystem analyses that involved a suite of integrated models embedded within a geographic information system. The goal of the exercise was to provide managers with maps that could illustrate probable shifts in vegetation, net primary production (NPP), and hydrologic responses associated with two selected climatic scenarios. The climatic scenarios were (a) a recent 12-yr record of weather data, and (b) a reconstituted set that sequentially introduced in repeated 3-yr intervals wetter–cooler, drier–warmer, and typical conditions. To extrapolate the implications of changes in ecosystem processes and resulting growth and distribution of vegetation and snowpack, the model incorporated geographic data. With underlying digital elevation maps, soil depth and texture, extrapolated climate, and current information on vegetation types and satellite-derived estimates of leaf area indices, simulations were extended to envision how the park might look after 120 yr. The predictions of change included underlying processes affecting the availability of water and nitrogen. Considerable field data were acquired to compare with model predictions under current climatic conditions. In general, the integrated landscape models of ecosystem processes had good agreement with measured NPP, snowpack, and streamflow, but the exercise revealed the difficulty and necessity of averaging point measurements across landscapes to achieve comparable results with modeled values. Under the extremely variable climate scenario significant changes in vegetation composition and growth as well as hydrologic responses were predicted across the park. In particular, a general rise in both the upper and lower limits of treeline was predicted. These shifts would probably occur along with a variety of disturbances (fire, insect, and disease outbreaks) as predictions of physiological stress (water, nutrients, light) altered competitive relations and hydrologic responses. The use of integrated landscape models applied in this exercise should provide managers with insights into the underlying processes important in maintaining community structure, and at the same time, locate where changes on the landscape are most likely to occur

Use of FLUXNET in the Community Land Model development

The Community Land Model version 3 (CLM3.0) simulates land-atmosphere exchanges in response to climatic forcings. CLM3.0 has known biases in the surface energy partitioning as a result of deficiencies in its hydrological and biophysical parameterizations. Such models, however, need to be robust for multidecadal global climate simulations. FLUXNET now provides an extensive data source of carbon, water and energy exchanges for investigating land processes, and it encompasses a global range of ecosystem-climate interactions. Data from 15 FLUXNET sites are used to identify and improve model deficiencies. Including a prognostic aquifer, a bare soil evaporation resistance formulation and numerous other changes in the model result in a significantly improved soil hydrology and energy partitioning. Terrestrial water storage increased by up to 300 mm in warm climates and decreased in cold climates. Nitrogen control of photosynthesis is revealed as another missing process in the model. These improvements increase the correlation coefficient of hourly and monthly latent heat fluxes from a range of 0.5–0.6 to the range of 0.7–0.9. RMSE of the simulated sensible heat fluxes decrease by 20–50%. Primary production is overestimated during the wet season in mediterranean and tropical ecosystems. This might be related to missing carbon-nitrogen dynamics as well as to site-specific parameters. The new model (CLM3.5) with an improved terrestrial water cycle should lead to more realistic land-atmosphere exchanges in coupled simulations. FLUXNET is found to be a valuable tool to develop and validate land surface models prior to their application in computationally expensive global simulations

Lessons on bridging the science-policy divide for climate change action in developing countries

Decision makers in developing countries need evidence of the impacts climate change is having and will continue to have on agriculture and food systems as well as knowledge on how to design better policies to deal with such impacts. Research for development scientists are generating this evidence but it might not always be what decision makers want or need. We present here a synthesis that is an attempt to learn lessons from projects conducted by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). These projects engaged with policy makers and other stakeholders by providing climate science and spaces for dialogue between researchers and decision makers for the purpose of improving climate change and agricultural policies. This study draws conclusions from across projects in five regions and confirms the presence of similar enablers to policy engagement and constraints to the use of scientific findings by policy makers in each region. The paper is guided by the following research questions: (a) What are the most effective means of science-policy engagement in the areas of climate change, food security, and agriculture?; (b) What are the enabling factors for research uptake in decision making?; and (c) What are the main constraints to policy engagement, and how can they be overcome? The Kaleidoscope Model for agricultural and food security policy change is used throughout the paper to help organize results and conceptualize the process of policy change. The CCAFS projects included in this study relied on sustained engagement between researchers and decision makers through a variety of means. Respondents from all regions indicated the importance of involving decision makers with the research process from the very beginning so that knowledge can be co-created and will meet the needs of the decision makers. The learning alliances and science-policy dialogue forums created through CCAFS projects proved successful in bringing together actors from multiple stakeholders and sectors. One of the key lessons from the CCAFS projects was that, rather than starting from scratch or trying to force review or revision of a policy that was not on anyone’s agenda, it was better to start by getting involved in a process that was already underway and look at how CCAFS could provide support and evidence. Major constraints faced by projects were the availability of decision makers to attend meetings and participate in project activities, staff turnover 4 within government ministries and departments, lack of time to engage, and the mismatch of political processes with research timelines

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Generating Daily Surfaces of Temperature and Precipitation over Complex Topography

We present the logical and algorithmic framework of a numerical model which generates daily interpolated surfaces of maximum air temperature, minimum air temperature, and precipitation over a gridded terrain, and we demonstrate its application in the state of Montana for a one-year period. The model generates daily estimates of the relationship between each meteorological variable and elevation for each grid cell in the terrain model and uses these estimates to generate interpolated surfaces of temperature and precipitation based on daily observations from a network of recording stations. Interpolates are based on a vertically exaggerated proximal polygon algorithm, in combination with a Gaussian-weighted spatial convolution kernel. Elevation relationships are estimated with a least squares weighted linear regression model that varies in space and in time by subsetting both the spatial and temporal domains. Standard lapse-rate regressions are used to model the relationship between maximum and minimum temperature and elevation. Precipitation relationships are based on a normalized difference algorithm. Predictions of precipitation occurrence employ a local event frequency statistic. We present some of the principal results of simulations across the state of Montana on a 1-km resolution grid for 1990. Cross-validation was used to generate daily predictions of maximum and minimum temperature and precipitation. Predicted annual averages were compared to observed annual averages, resulting in mean absolute errors for daily prediction of maximum and minimum temperatures of 0.69 degrees and 0.98 degrees Celsius/day, respectively. Mean absolute errors for predicted precipitation were 0.03 cm/day (11.83 cm/year, or 20.0% measured as a proportion of total annual precipitation)