Ryan Bright

Seniorforsker

(+47) 974 77 997
ryan.bright@nibio.no

Sted
Ås - Bygg H8

Besøksadresse
Høgskoleveien 8, 1433 Ås

Biografi

Forskningsinteresser: - Økologisk klimatologi - Albedo dynamikk i boreale skoger - Landoverflate modellering - Klimaberegninger for skogbruk og annen arealbruk Seneste publikasjoner

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Sammendrag

Surface albedo is an important physical attribute of the climate system and satellite retrievals are useful for understanding how it varies in time and space. Surface albedo is sensitive to land cover and structure, which can vary considerably within the area comprising the effective spatial resolution of the satellite-based retrieval. This is particularly true for MODIS products and for topographically complex regions, such as Norway, which makes it difficult to separate the environmental drivers (e.g., temperature and snow) from those related to land cover and vegetation structure. In the present study, we employ high resolution datasets of Norwegian land cover and structure to spectrally unmix MODIS surface albedo retrievals (MCD43A3 v6) to study how surface albedo varies with land cover and structure. Such insights are useful for constraining land cover-dependent albedo parameterizations in models employed for regional climate or hydrological research and for developing new empirical models. At the scale of individual land cover types, we found that the monthly surface albedo can be predicted at a high accuracy when given additional information about forest structure, snow cover, and near surface air temperature. Such predictions can provide useful empirical benchmarks for climate model predictions made at the land cover level, which is critical for instilling greater confidence in the albedo-related climate impacts of anthropogenic land use/land cover change (LULCC).

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Satellite time-series data are bolstering global change research, but their use to elucidate land changes and vegetation dynamics is sensitive to algorithmic choices. Different algorithms often give inconsistent or sometimes conflicting interpretations of the same data. This lack of consensus has adverse implications and can be mitigated via ensemble modeling, an algorithmic paradigm that combines many competing models rather than choosing only a single “best” model. Here we report one such time-series decomposition algorithm for deriving nonlinear ecosystem dynamics across multiple timescales—A Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST). As an ensemble algorithm, BEAST quantifies the relative usefulness of individual decomposition models, leveraging all the models via Bayesian model averaging. We tested it upon simulated, Landsat, and MODIS data. BEAST detected changepoints, seasonality, and trends in the data reliably; it derived realistic nonlinear trends and credible uncertainty measures (e.g., occurrence probability of changepoints over time)—some information difficult to derive by conventional single-best-model algorithms but critical for interpretation of ecosystem dynamics and detection of low-magnitude disturbances. The combination of many models enabled BEAST to alleviate model misspecification, address algorithmic uncertainty, and reduce overfitting. BEAST is generically applicable to time-series data of all kinds. It offers a new analytical option for robust changepoint detection and nonlinear trend analysis and will help exploit environmental time-series data for probing patterns and drivers of ecosystem dynamics.

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Due to the potential for land-use–land-cover change (LULCC) to alter surface albedo, there is need within the LULCC science community for simple and transparent tools for predicting radiative forcings (ΔF) from surface albedo changes (Δαs). To that end, the radiative kernel technique – developed by the climate modeling community to diagnose internal feedbacks within general circulation models (GCMs) – has been adopted by the LULCC science community as a tool to perform offline ΔF calculations for Δαs. However, the codes and data behind the GCM kernels are not readily transparent, and the climatologies of the atmospheric state variables used to derive them vary widely both in time period and duration. Observation-based kernels offer an attractive alternative to GCM-based kernels and could be updated annually at relatively low costs. Here, we present a radiative kernel for surface albedo change founded on a novel, simplified parameterization of shortwave radiative transfer driven with inputs from the Clouds and the Earth's Radiant Energy System (CERES) Energy Balance and Filled (EBAF) products. When constructed on a 16-year climatology (2001–2016), we find that the CERES-based albedo change kernel – or CACK – agrees remarkably well with the mean kernel of four GCMs (rRMSE = 14 %). When the novel parameterization underlying CACK is applied to emulate two of the GCM kernels using their own boundary fluxes as input, we find even greater agreement (mean rRMSE = 7.4 %), suggesting that this simple and transparent parameterization represents a credible candidate for a satellite-based alternative to GCM kernels. We document and compute the various sources of uncertainty underlying CACK and include them as part of a more extensive dataset (CACK v1.0) while providing examples showcasing its application.

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Vegetation optical properties have a direct impact on canopy absorption and scattering and are thus needed for modeling surface fluxes. Although plant functional type (PFT) classification varies between different land surface models (LSMs), their optical properties must be specified. The aim of this study is to revisit the “time-invariant optical properties table” of the Simple Biosphere (SiB) model (later referred to as the “SiB table”) presented 30 years ago by Dorman and Sellers (1989), which has since been adopted by many LSMs. This revisit was needed as many of the data underlying the SiB table were not formally reviewed or published or were based on older papers or on personal communications (i.e., the validity of the optical property source data cannot be inspected due to missing data sources, outdated citation practices, and varied estimation methods). As many of today's LSMs (e.g., the Community Land Model (CLM), the Jena Scheme of Atmosphere Biosphere Coupling in Hamburg (JSBACH), and the Joint UK Land Environment Simulator (JULES)) either rely on the optical properties of the SiB table or lack references altogether for those they do employ, there is a clear need to assess (and confirm or correct) the appropriateness of those being used in today's LSMs. Here, we use various spectral databases to synthesize and harmonize the key optical property information of PFT classification shared by many leading LSMs. For forests, such classifications typically differentiate PFTs by broad geo-climatic zones (i.e., tropical, boreal, temperate) and phenology (i.e., deciduous vs. evergreen). For short-statured vegetation, such classifications typically differentiate between crops, grasses, and photosynthetic pathway. Using the PFT classification of the CLM (version 5) as an example, we found the optical properties of the visible band (VIS; 400–700 nm) to fall within the range of measured values. However, in the near-infrared and shortwave infrared bands (NIR and SWIR; e.g., 701–2500 nm, referred to as “NIR”) notable differences between CLM default and measured values were observed, thus suggesting that NIR optical properties are in need of an update. For example, for conifer PFTs, the measured mean needle single scattering albedo (SSA, i.e., the sum of reflectance and transmittance) estimates in NIR were 62 % and 78 % larger than the CLM default parameters, and for PFTs with flat leaves, the measured mean leaf SSA values in NIR were 20 %, 14 %, and 19 % larger than the CLM defaults. We also found that while the CLM5 PFT-dependent leaf angle values were sufficient for forested PFTs and grasses, for crop PFTs the default parameterization appeared too vertically oriented, thus warranting an update. In addition, we propose using separate bark reflectance values for conifer and deciduous PFTs and demonstrate how shoot-level clumping correction can be incorporated into LSMs to mitigate violations of turbid media assumption and Beer's law caused by the nonrandomness of finite-sized foliage elements.

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Deforestation influences surface properties such as surface roughness, resulting in changes in the surface energy balance and surface temperature. Recent studies suggest that the biogeophysical effects are dominated by changing roughness, and it remains unclear whether this can be reconciled with earlier modeling studies that highlighted the importance of a reduction of evapotranspiration in the low latitudes and a reduction of net shortwave radiation at the surface in the high latitudes. To clarify this situation, we analyze the local effects of deforestation on surface energy balance and temperature in the MPI‐ESM climate model by performing three separate experiments: switching from forest to grass all surface properties, only surface albedo, and only surface roughness. We find that the locally induced changes in surface temperature are dominated by changes in surface roughness for the annual mean, the response of the diurnal amplitude, and the seasonal response to deforestation. For these three quantities, the results of the MPI‐ESM lie within the range of observation‐based data sets. Deforestation‐induced decreases in surface roughness contribute substantially to winter cooling in the boreal regions and to decreases in evapotranspiration in the tropics. By comparing the energy balance decompositions from the three experiments, the view that roughness changes dominate the biogeophysical consequences of deforestation can be reconciled with the earlier studies highlighting the relevance of evapotranspiration.

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Climate change could increase fire risk across most of the managed boreal forest. Decreasing this risk by increasing the proportion of broad-leaved tree species is an overlooked mitigation–adaption strategy with multiple benefits.

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Forest management affects the distribution of tree species and the age class of a forest, shaping its overall structure and functioning and in turn the surface–atmosphere exchanges of mass, energy, and momentum. In order to attribute climate effects to anthropogenic activities like forest management, good accounts of forest structure are necessary. Here, using Fennoscandia as a case study, we make use of Fennoscandic National Forest Inventory (NFI) data to systematically classify forest cover into groups of similar aboveground forest structure. An enhanced forest classification scheme and related lookup table (LUT) of key forest structural attributes (i.e., maximum growing season leaf area index (LAImax), basal-area-weighted mean tree height, tree crown length, and total stem volume) was developed, and the classification was applied for multisource NFI (MSNFI) maps from Norway, Sweden, and Finland. To provide a complete surface representation, our product was integrated with the European Space Agency Climate Change Initiative Land Cover (ESA CCI LC) map of present day land cover (v.2.0.7). Comparison of the ESA LC and our enhanced LC products (https://doi.org/10.21350/7zZEy5w3) showed that forest extent notably (κ = 0.55, accuracy 0.64) differed between the two products. To demonstrate the potential of our enhanced LC product to improve the description of the maximum growing season LAI (LAImax) of managed forests in Fennoscandia, we compared our LAImax map with reference LAImax maps created using the ESA LC product (and related cross-walking table) and PFT-dependent LAImax values used in three leading land models. Comparison of the LAImax maps showed that our product provides a spatially more realistic description of LAImax in managed Fennoscandian forests compared to reference maps. This study presents an approach to account for the transient nature of forest structural attributes due to human intervention in different land models.

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Predicting the surface albedo of a forest of a given species composition or plant functional type is complicated by the wide range of structural attributes it may display. Accurate characterizations of forest structure are therefore essential to reducing the uncertainty of albedo predictions in forests, particularly in the presence of snow. At present, forest albedo parameterizations remain a nonnegligible source of uncertainty in climate models, and the magnitude attributable to insufficient characterization of forest structure remains unclear. Here we employ a forest classification scheme based on the assimilation of Fennoscandic (i.e., Norway, Sweden, and Finland) national forest inventory data to quantify the magnitude of the albedo prediction error attributable to poor characterizations of forest structure. For a spatial domain spanning ~611,000 km2 of boreal forest, we find a mean absolute wintertime (December–March) albedo prediction error of 0.02, corresponding to a mean absolute radiative forcing ~0.4 W/m2. Further, we evaluate the implication of excluding albedo trajectories linked to structural transitions in forests during transient simulations of anthropogenic land use/land cover change. We find that, for an intensively managed forestry region in southeastern Norway, neglecting structural transitions over the next quarter century results in a foregone (undetected) radiatively equivalent impact of ~178 Mt‐CO2‐eq. year−1 on average during this period—a magnitude that is roughly comparable to the annual greenhouse gas emissions of a country such as The Netherlands. Our results affirm the importance of improving the characterization of forest structure when simulating surface albedo and associated climate effects.

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Following a land cover and land management change (LCMC), local surface temperature responds to both a change in available energy and a change in the way energy is redistributed by various non-radiative mechanisms. However, the extent to which non-radiative mechanisms contribute to the local direct temperature response for different types of LCMC across the world remains uncertain. Here, we combine extensive records of remote sensing and in situ observation to show that non-radiative mechanisms dominate the local response in most regions for eight of nine common LCMC perturbations. We find that forest cover gains lead to an annual cooling in all regions south of the upper conterminous United States, northern Europe, and Siberia—reinforcing the attractiveness of re-/afforestation as a local mitigation and adaptation measure in these regions. Our results affirm the importance of accounting for non-radiative mechanisms when evaluating local land-based mitigation or adaptation policies.

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An 11-year remotely sensed surface albedo dataset coupled with historical meteorological and stand-level forest management data for a variety of stands in Norway’s most productive logging region is used to develop regression models describing temporal changes in forest albedo following clear-cut harvest disturbance events. Datasets are grouped by dominant tree species, and two alternate multiple regression models are developed and tested following a potential-modifier approach. This result in models with statistically significant parameters (p < 0.05) that explain a large proportion of the observed variation, requiring a single canopy modifier predictor coupled with either monthly or annual mean air temperature as a predictor of a stand’s potential albedo. Models based on annual mean temperature predict annual albedo with errors (RMSE) in the range of 0.025–0.027, while models based on monthly mean temperature predict monthly albedo with errors ranging between of 0.057–0.065 depending on the dominant tree species. While both models have the potential to be transferable to other boreal regions with similar forest management regimes, further validation efforts are required. As active management of boreal forests is increasingly seen as a means to mitigate climate change, the presented models can be used with routine forest inventory and meteorological data to predict albedo evolution in managed forests throughout the region, which, together with carbon cycle modeling, can lead to more holistic climate impact assessments of alternative forest harvest scenarios and forest product systems.