Land Climate Models Research Articles

The Database of European Forest Insect and Disease Disturbances: DEFID2.

Insect and disease outbreaks in forests are biotic disturbances that can profoundly alter ecosystem dynamics. In many parts of the world, these disturbance regimes are intensifying as the climate changes and shifts the distribution of species and biomes. As a result, key forest ecosystem services, such as carbon sequestration, regulation of water flows, wood production, protection of soils, and the conservation of biodiversity, could be increasingly compromised. Despite the relevance of these detrimental effects, there are currently no spatially detailed databases that record insect and disease disturbances on forests at the pan-European scale. Here, we present the new Database of European Forest Insect and Disease Disturbances (DEFID2). It comprises over 650,000 harmonized georeferenced records, mapped as polygons or points, of insects and disease disturbances that occurred between 1963 and 2021 in European forests. The records currently span eight different countries and were acquired through diverse methods (e.g., ground surveys, remote sensing techniques). The records in DEFID2 are described by a set of qualitative attributes, including severity and patterns of damage symptoms, agents, host tree species, climate-driven trigger factors, silvicultural practices, and eventual sanitary interventions. They are further complemented with a satellite-based quantitative characterization of the affected forest areas based on Landsat Normalized Burn Ratio time series, and damage metrics derived from them using the LandTrendr spectral-temporal segmentation algorithm (including onset, duration, magnitude, and rate of the disturbance), and possible interactions with windthrow and wildfire events. The DEFID2 database is a novel resource for many large-scale applications dealing with biotic disturbances. It offers a unique contribution to design networks of experiments, improve our understanding of ecological processes underlying biotic forest disturbances, monitor their dynamics, and enhance their representation in land-climate models. Further data sharing is encouraged to extend and improve the DEFID2 database continuously. The database is freely available at https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/FOREST/DISTURBANCES/DEFID2/.

Large-scale reforestation can increase water yield and reduce drought risk for water-insecure regions in the Asia-Pacific.

Large-scale reforestation can potentially bring both benefits and risks to the water cycle, which needs to be better quantified under future climates to inform reforestation decisions. We identified 477 water-insecure basins worldwide accounting for 44.6% (380.2Mha) of the global reforestation potential. As many of these basins are in the Asia-Pacific, we used regional coupled land-climate modeling for the period 2041-2070 to reveal that reforestation increases evapotranspiration and precipitation for most water-insecure regions over the Asia-Pacific. This resulted in a statistically significant increase in water yield (p<.05) for the Loess Plateau-North China Plain, Yangtze Plain, Southeast China, and Irrawaddy regions. Precipitation feedback was influenced by the degree of initial moisture limitation affecting soil moisture response and thus evapotranspiration, as well as precipitation advection from other reforested regions and moisture transport away from the local region. Reforestation also reduces the probability of extremely dry months in most of the water-insecure regions. However, some regions experience nonsignificant declines in net water yield due to heightened evapotranspiration outstripping increases in precipitation, or declines in soil moisture and advected precipitation.

The European forest carbon budget under future climate conditions and current management practices

Abstract. To become carbon neutral by 2050, the European Union (EU27) net carbon sink from forests should increase from the current level of about −360 to −450 Mt CO2eq yr−1 by 2050. Reaching this target requires additional efforts, which should be informed by the expected interactions between current age-class distributions, the effect of forest management practices and the expected impacts of future climate change. However, modelling the combined effect of these drivers is challenging, since it requires a mechanistic assessment of climate impacts on primary productivity and heterotrophic respiration and a detailed representation of the forest age structure and of the management practices across the entire EU. To achieve this goal, we combined the output provided by four land–climate models – run under two different Representative Concentration Pathway scenarios (RCP2.6 and RCP6.0) – to parameterize the input data used in an empirical forest growth model. This hybrid modelling approach aims to quantify the impact of climate change and forest management on the long-term (i.e. to 2100) evolution of the EU27 + UK forest carbon budget. This approach was tested using a business-as-usual (BAU) scenario, based on the continuation of the management practices applied by EU member states within the historical period 2000–2015. We emphasize that our study does not explore a specific policy scenario but describes a methodological framework. Our results highlight that, under our BAU case, the EU27 + UK forest C sink would decrease to about −250 Mt CO2eq yr−1 in 2050 and −80 Mt CO2eq yr−1 by 2100. The main driver of the long-term evolution of the forest C sink is the ongoing ageing process of the European forests, mostly determined by past and ongoing management. In addition, climate change may further amplify or mitigate this trend. Due to the large uncertainty in climate projections, in 2050 the net C sink may range from −100 to −400 Mt CO2eq yr−1 under RCP2.6 and from −100 to −300 Mt CO2eq yr−1 under RCP6.0. These results suggest that while a change in management practices would be needed to reverse an otherwise declining trend in the sink, climate change adds a considerable uncertainty, potentially nearly doubling or halving the sink associated with management.

Impact of Earth Greening on the Terrestrial Water Cycle

Leaf area index (LAI) is increasing throughout the globe, implying Earth greening. Global modeling studies support this contention, yet satellite observations and model simulations have never been directly compared. Here, for the first time, a coupled land–climate model was used to quantify the potential impact of the satellite-observed Earth greening over the past 30 years on the terrestrial water cycle. The global LAI enhancement of 8% between the early 1980s and the early 2010s is modeled to have caused increases of 12.0 ± 2.4 mm yr−1 in evapotranspiration and 12.1 ± 2.7 mm yr−1 in precipitation—about 55% ± 25% and 28% ± 6% of the observed increases in land evapotranspiration and precipitation, respectively. In wet regions, the greening did not significantly decrease runoff and soil moisture because it intensified moisture recycling through a coincident increase of evapotranspiration and precipitation. But in dry regions, including the Sahel, west Asia, northern India, the western United States, and the Mediterranean coast, the greening was modeled to significantly decrease soil moisture through its coupling with the atmospheric water cycle. This modeled soil moisture response, however, might have biases resulting from the precipitation biases in the model. For example, the model dry bias might have underestimated the soil moisture response in the observed dry area (e.g., the Sahel and northern India) given that the modeled soil moisture is near the wilting point. Thus, an accurate representation of precipitation and its feedbacks in Earth system models is essential for simulations and predictions of how soil moisture responds to LAI changes, and therefore how the terrestrial water cycle responds to climate change.

Estimating the Anthropogenic Sea Surface Temperature Response Using Pattern Scaling

Abstract This study seeks to derive the sea surface temperature (SST) response to anthropogenic forcing from observations over the last century, using simple methods inspired from pattern scaling. As in pattern scaling, the spatial response is assumed to scale with global-mean and annual-mean surface temperature. The long-term aim of this work is to generate anthropogenically forced SST and sea ice patterns for the recent past and near-term future, and use them to force atmosphere–land climate models for attribution and prediction purposes. The present work compares estimation methodologies and, within a Monte Carlo framework based on large initial condition ensembles of climate model simulations, examines the robustness of the patterns obtained. The different methods explored here yield a similar SST spatial response, mostly reflecting the observed SST linear trend map. The different methods nevertheless provide distinctive temporal evolution of the global-mean and annual-mean SST response, which in turn affects the temporal evolution of the global-mean and annual-mean air surface temperature simulated in corresponding prescribed SST simulations. The estimated SST spatial response consists mostly of a warming of the midlatitude coasts near the western boundary currents, the tropical Indian Ocean, and the Arctic Ocean. This pattern generally agrees with previously published observational and modeling studies. Based on Monte Carlo analysis of the large ensembles, it is found that between 36% and 56% of its spatial variance results from anthropogenic forcing. Overall, the work herein provides constraints on the uncertainty associated with the spatial variability of an anthropogenically forced component of climate change derived from observations, which can potentially be used for climate attribution and prediction.

Land-based mitigation in climate stabilization

This paper evaluates the role of land in long-run climate stabilization mitigation scenarios. The details of land modeling for common stabilization policy scenarios are, for the first time, presented, contrasted, and assessed. While we find significant differences in approaches across modeling platforms, all the approaches conclude that land based mitigation – agriculture, forestry, and biomass liquid and solid energy substitutes – could be a steady and significant part of the cost-effective portfolio of mitigation strategies; thereby, reducing stabilization cost and increasing flexibility for achieving more aggressive climate targets. However, large fossil fuel emissions reductions are still required, and there are substantial uncertainties, with little agreement about abatement magnitudes. Across the scenarios, land mitigation options contribute approximately 100 to 340 GtC equivalent abatement over the century, 15 to 40% of the total required for stabilization, with bio-energy providing up to 15% of total primary energy. Long-run land climate modeling is rapidly evolving with critical challenges to address. In characterizing current capability, this paper hopes to stimulate future research and the next generation of land modeling and provide a point of comparison for energy and climate policies considering bio-energy, reduced deforestation and degradation, and cost containment.

The atmospheric boundary layer structure over the open and ice-covered Baltic Sea: in situ measurements compared to simulations with the regional model REMO

The regional model REMO, which is the atmospheric component of the coupled atmosphere–ice–ocean–land climate model system BALTIMOS, is tested with respect to its ability to simulate the atmospheric boundary layer over the open and ice-covered Baltic Sea. REMO simulations are compared to ship, radiosonde, and aircraft observations taken during eight field experiments. The main results of the comparisons are: (1) The sharpness and strength of the temperature inversion are underestimated by REMO. Over open water, this is connected with an overestimation of cloud coverage and moisture content above the inversion. (2) The vertical temperature stratification in the lowest 200 m over sea ice is too stable. (3) The horizontal inhomogeneity of sea ice concentration as observed by aircraft could not be properly represented by the prescribed ice concentration in REMO; large differences in the surface heat fluxes arise especially under cold-air advection conditions. The results of the comparisons suggest a reconsideration of the parameterization of subgrid-scale vertical exchange both under unstable und stable conditions.

Biophysical Evaluation of Land-Cover Products for Land–Climate Modeling

Abstract The need for accurate characterization of the land surface as boundary conditions in climate models has been recognized widely in the climate modeling community. A large number of land-cover datasets are currently used in climate models either to better represent surface conditions or to study the impacts of surface changes. Deciding upon land-cover datasets can be challenging because the datasets are made with different sensors, ranging methodologies, and varying classification objectives. A new statistical measure Q was developed to evaluate land-cover datasets in land–climate interaction research. This measure calculates biophysical precision of land-cover datasets using 1-km monthly Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) product. This method aggregates within-class biophysical consistency, calculated as LAI variation, across a study domain and over multiple years into a single statistic. A smaller mean Q value for a land-cover product indicates more precise biophysical characterization within the classes. As an illustration, four land-cover products were assessed in the East Africa region: Global Land Cover 2000 (GLC2000), MODIS land cover, Olson Global Ecosystems (OGE), and Land Ecosystem–Atmosphere Feedback (LEAF) model. The evaluation was conducted at three different spatial scales corresponding to 30 × 30, 50 × 50, and 100 × 100 km quadrates. The Q measure found that GLC2000 ranked higher compared to the other three land-cover products for every quadrate size. For the 30 × 30 km quadrate size GLC2000 was significantly better than LEAF, which is currently used in the Regional Atmospheric Modeling System. The statistic ranks MODIS land cover above OGE, which is above LEAF. As quadrate size increases, differences between Q decrease indicating greater uncertainty at coarser resolution. The utility of the measure is that it can be applied to any continuous parameter over any scale (space or time) to evaluate the biophysical precision of any land-cover dataset.

Road building, land use and climate change: prospects for environmental governance in the Amazon

Some coupled land–climate models predict a dieback of Amazon forest during the twenty-first century due to climate change, but human land use in the region has already reduced the forest cover. The causation behind land use is complex, and includes economic, institutional, political and demographic factors. Pre-eminent among these factors is road building, which facilitates human access to natural resources that beget forest fragmentation. While official government road projects have received considerable attention, unofficial road building by interest groups is expanding more rapidly, especially where official roads are being paved, yielding highly fragmented forest mosaics. Effective governance of natural resources in the Amazon requires a combination of state oversight and community participation in a ‘hybrid’ model of governance. The MAP Initiative in the southwestern Amazon provides an example of an innovative hybrid approach to environmental governance. It embodies a polycentric structure that includes government agencies, NGOs, universities and communities in a planning process that links scientific data to public deliberations in order to mitigate the effects of new infrastructure and climate change.