| 1. |
- Berg, Alexis, et al.
(författare)
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Interannual Coupling between Summertime Surface Temperature and Precipitation over Land: Processes and Implications for Climate Change
- 2015
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Ingår i: Journal of Climate. - 1520-0442. ; 28:3, s. 1308-1328
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Tidskriftsartikel (refereegranskat)abstract
- Widespread negative correlations between summertime-mean temperatures and precipitation over land regions are a well-known feature of terrestrial climate. This behavior has generally been interpreted in the context of soil moisture atmosphere coupling, with soil moisture deficits associated with reduced rainfall leading to enhanced surface sensible heating and higher surface temperature. The present study revisits the genesis of these negative temperature precipitation correlations using simulations from the Global Land Atmosphere Coupling Experiment phase 5 of the Coupled Model Intercomparison Project (GLACE-CMIP5) multimodel experiment. The analyses are based on simulations with five climate models, which were integrated with prescribed (noninteractive) and with interactive soil moisture over the period 1950-2100. While the results presented here generally confirm the interpretation that negative correlations between seasonal temperature and precipitation arise through the direct control of soil moisture on surface heat flux partitioning, the presence of widespread negative correlations when soil moisture atmosphere interactions are artificially removed in at least two out of five models suggests that atmospheric processes, in addition to land surface processes, contribute to the observed negative temperature precipitation correlation. On longer time scales, the negative correlation between precipitation and temperature is shown to have implications for the projection of climate change impacts on near-surface climate: in all models, in the regions of strongest temperature precipitation anticorrelation on interannual time scales, long-term regional warming is modulated to a large extent by the regional response of precipitation to climate change, with precipitation increases (decreases) being associated with minimum (maximum) warming. This correspondence appears to arise largely as the result of soil moisture atmosphere interactions.
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| 2. |
- Yazbeck, Theresia, et al.
(författare)
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Site Characteristics Mediate the Relationship Between Forest Productivity and Satellite Measured Solar Induced Fluorescence
- 2021
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Ingår i: Frontiers in Forests and Global Change. - : Frontiers Media SA. - 2624-893X. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- Solar-Induced Chlorophyll Fluorescence (SIF) can provide key information about the state of photosynthesis and offers the prospect of defining remote sensing-based estimation of Gross Primary Production (GPP). There is strong theoretical support for the link between SIF and GPP and this relationship has been empirically demonstrated using ground-based, airborne, and satellite-based SIF observations, as well as modeling. However, most evaluations have been based on monthly and annual scales, yet the GPP:SIF relations can be strongly influenced by both vegetation structure and physiology. At the monthly timescales, the structural response often dominates but short-term physiological variations can strongly impact the GPP:SIF relations. Here, we test how well SIF can predict the inter-daily variation of GPP during the growing season and under stress conditions, while taking into account the local effect of sites and abiotic conditions. We compare the accuracy of GPP predictions from SIF at different timescales (half-hourly, daily, and weekly), while evaluating effect of adding environmental variables to the relationship. We utilize observations for years 2018–2019 at 31 mid-latitudes, forested, eddy covariance (EC) flux sites in North America and Europe and use TROPOMI satellite data for SIF. Our results show that SIF is a good predictor of GPP, when accounting for inter-site variation, probably due to differences in canopy structure. Seasonally averaged leaf area index, fraction of absorbed photosynthetically active radiation (fPAR) and canopy conductance provide a predictor to the site-level effect. We show that fPAR is the main factor driving errors in the linear model at high temporal resolution. Adding water stress indicators, namely canopy conductance, to a multi-linear SIF-based GPP model provides the best improvement in the model precision at the three considered timescales, showing the importance of accounting for water stress in GPP predictions, independent of the SIF signal. SIF is a promising predictor for GPP among other remote sensing variables, but more focus should be placed on including canopy structure, and water stress effects in the relationship, especially when considering intra-seasonal, and inter- and intra-daily resolutions.
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| 3. |
- Zhang, Yao, et al.
(författare)
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Spatio-Temporal Convergence of Maximum Daily Light-Use Efficiency Based on Radiation Absorption by Canopy Chlorophyll
- 2018
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Ingår i: Geophysical Research Letters. - 0094-8276. ; 45:8, s. 3508-3519
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Tidskriftsartikel (refereegranskat)abstract
- Light-use efficiency (LUE), which quantifies the plants' efficiency in utilizing solar radiation for photosynthetic carbon fixation, is an important factor for gross primary production estimation. Here we use satellite-based solar-induced chlorophyll fluorescence as a proxy for photosynthetically active radiation absorbed by chlorophyll (APARchl) and derive an estimation of the fraction of APARchl (fPARchl) from four remotely sensed vegetation indicators. By comparing maximum LUE estimated at different scales from 127 eddy flux sites, we found that the maximum daily LUE based on PAR absorption by canopy chlorophyll ( εmaxchl), unlike other expressions of LUE, tends to converge across biome types. The photosynthetic seasonality in tropical forests can also be tracked by the change of fPARchl, suggesting the corresponding εmaxchl to have less seasonal variation. This spatio-temporal convergence of LUE derived from fPARchl can be used to build simple but robust gross primary production models and to better constrain process-based models.
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