Future Projections of EURO-CORDEX Raw and Bias-Corrected Daily Maximum Wind Speeds Over Scandinavia

Postponed access: the file will be available after 2023-08-21Twenty historical and future Coordinated Regional Climate Downscaling Experiment ensemble for Europe simulations are bias corrected to investigate the future changes in the daily maximum wind speed over Scandinavia. We use quantile mapping to adjust the wind for the historical period (1985–2014) and quantile delta mapping for two future periods (2041–2070, 2071–2100, RCP8.5) with the 3-km spatial resolution NORA3 hindcast as the reference data set. Decomposing the variance, we find that most of the inter-model spread in the bias and the response to climate change is due to the regional climate model over land and mainly to the general climate model over sea. On average, the mean daily maximum wind speed is projected to increase everywhere except along the western coast of Norway and Denmark. In summer, we see an opposite sign over Sweden and Finland. The Norwegian Sea experiences weaker mean and high wind whereas the Baltic Sea experiences a strengthening. Bias correction influences the amplitude of the response, not the response pattern. Wind speed distributions can have different shapes in the future, with, for example, a flattening of the distribution off the coast of Norway with more frequent weak and very strong winds. Apart from a few locations in Norway, we find an increase in the number of days in the local highest historical wind category. Overall, summer exhibits opposite signals compared to the three other seasons. At country scale, the sign of the change in the mean and 98th percentile varies greatly depending on the region and among the simulations, especially for Norway.publishedVersio

Why has Precipitation Increased in the Last 120 Years in Norway?

We use a data set with daily precipitation observations from 55 homogeneity-tested stations in Norway from 1900 to 2019 available from MET-Norway. These observations show that precipitation in Norway has increased by 19% since 1900. Notably, over half of the overall increase occurred within the decade of 1980–1990 and is happening across all precipitation rates. To examine possible mechanisms behind the precipitation increase, we use a diagnostic model to separate the effects of changes in vertical velocity, temperature and relative humidity. We use daily vertical velocity, near-surface temperature and relative humidity from two reanalysis products, ERA-20C and 20th Century Reanalysis. The model-based precipitation correlates significantly with the observed precipitation on an annual timescale (r > 0.9), as well as captures the trend in all reanalysis products. The diagnostic model indicates that the variability in vertical velocity chiefly determines the interannual variability and long-term trend. The trend in vertical velocities contributes to more than 80% of the total modeled trend in precipitation between 1900 and 2019. However, over the last two decades (1995–2015), changes in temperature and relative humidity are the main contributors to the modeled trend in precipitation.publishedVersio

Bruk og misbruk av klimaframskrivninger

Forskere er avhengig av pålitelige klimaframskrivninger for å kunne si hvilke konsekvenser klimaforandringer får for mennesker og samfunn i framtiden. Men hva kan klimaframskrivninger brukes til og hvordan bør de brukes?publishedVersio

NORA3-WP: A high-resolution offshore wind power dataset for the Baltic, North, Norwegian, and Barents Seas

We present a new high resolution wind resource and wind power dataset named NORA3-WP. The dataset covers the North Sea, the Baltic Sea and parts of the Norwegian and Barents Seas. The 3-km Norwegian reanalysis (NORA3) forms the basis for the new dataset. NORA3-WP is an open access dataset intended for use in research, governmental management and for stakeholders to attain relevant wind resource and wind power information in the planning phase of a new wind farm project. The variables are available as monthly data, and provides a climatological overview of 25 wind resource and wind power related variables for three selected turbines for the ocean areas surrounding Norway. In addition, the underlying hourly wind speed data and hourly wind power generation for three selected turbines are also available for higher frequency analysis and case-studies.publishedVersio

Synoptic Conditions and Moisture Sources Actuating Extreme Precipitation in Nepal

Despite the vast literature on heavy‐precipitation events in South Asia, synoptic conditions and moisture sources related to extreme precipitation in Nepal have not been addressed systematically. We investigate two types of synoptic conditions—low‐pressure systems and midlevel troughs—and moisture sources related to extreme precipitation events. To account for the high spatial variability in rainfall, we cluster station‐based daily precipitation measurements resulting in three well‐separated geographic regions: west, central, and east Nepal. For each region, composite analysis of extreme events shows that atmospheric circulation is directed against the Himalayas during an extreme event. The direction of the flow is regulated by midtropospheric troughs and low‐pressure systems traveling toward the respective region. Extreme precipitation events feature anomalous high abundance of total column moisture. Quantitative Lagrangian moisture source diagnostic reveals that the largest direct contribution stems from land (approximately 75%), where, in particular, over the Indo‐Gangetic Plain moisture uptake was increased. Precipitation events occurring in this region before the extreme event likely provided additional moisture.publishedVersio

Recent and future changes of the Arctic sea-ice cover

The present and future state of the Arctic sea ice cover is explored using new observations and a coupled one dimensional air–sea–ice model. Updated satellite observations of Fram Strait ice-area export show an increase over the last four years, with 37% increase in winter 07–08. Atmospheric poleward energy flux declined since 1990, but advection of oceanic heat has recently increased. Simulations show that the ice area export is a stronger driver of thinning than the estimated ocean heat fluxes of 40 TW. Increased ocean heat transport will raise primarily Atlantic layer temperature. The ‘present 2007’ state of the Arctic ice could be a stable state given the recent high ice area export, but if ocean heat advection and ice export decrease, the ice cover will recover. A 2*CO2 scenario with export and oceanic heat flux remaining strong, forecasts a summer Arctic open ocean area of 95% around 2050.publishedVersio

The 3 km Norwegian reanalysis (NORA3) – a validation of offshore wind resources in the North Sea and the Norwegian Sea

We validate a new high-resolution (3 km) numerical mesoscale weather simulation for offshore wind power purposes for the time period 2004–2016 for the North Sea and the Norwegian Sea. The 3 km Norwegian reanalysis (NORA3) is a dynamically downscaled data set, forced with state-of-the-art atmospheric reanalysis as boundary conditions. We conduct an in-depth validation of the simulated wind climatology towards the observed wind climatology to determine whether NORA3 can serve as a wind resource data set in the planning phase of future offshore wind power installations. We place special emphasis on evaluating offshore wind-power-related metrics and the impact of simulated wind speed deviations on the estimated wind power and the related variability. We conclude that the NORA3 data are well suited for wind power estimates but give slightly conservative estimates of the offshore wind metrics. In other words, wind speeds in NORA3 are typically 5 % (0.5 m s−1) lower than observed wind speeds, giving an underestimation of offshore wind power of 10 %–20 % (equivalent to an underestimation of 3 percentage points in the capacity factor) for a selected turbine type and hub height. The model is biased towards lower wind power estimates due to overestimation of the wind speed events below typical wind speed limits of rated wind power (u11–13 m s−1). The hourly wind speed and wind power variability are slightly underestimated in NORA3. However, the number of hours with zero power production caused by the wind conditions (around 12 % of the time) is well captured, while the duration of each of these events is slightly overestimated, leading to 25-year return values for zero-power duration being too high for the majority of the sites. The model performs well in capturing spatial co-variability in hourly wind power production, with only small deviations in the spatial correlation coefficients among the sites. We estimate the observation-based decorrelation length to be 425.3 km, whereas the model-based length is 19 % longer.publishedVersio

Recent wind driven high sea ice area export in the Fram Strait contributes to Arctic sea ice decline

Arctic sea ice area has been decreasing for the past two decades. Apart from melting, the southward drift through Fram Strait is the main ice loss mechanism. We present high resolution sea ice drift data across 79° N from 2004 to 2010. Ice drift has been derived from radar satellite data and corresponds well with variability in local geostrophic wind. The underlying East Greenland current contributes with a constant southward speed close to 5 cm s−1, and drives around a third of the ice export. We use geostrophic winds derived from reanalysis data to calculate the Fram Strait ice area export back to 1957, finding that the sea ice area export recently is about 25% larger than during the 1960's. The increase in ice export occurred mostly during winter and is directly connected to higher southward ice drift velocities, due to stronger geostrophic winds. The increase in ice drift is large enough to counteract a decrease in ice concentration of the exported sea ice. Using storm tracking we link changes in geostrophic winds to more intense Nordic Sea low pressure systems. Annual sea ice area export likely has a significant influence on the summer sea ice variability and we find low values in the 1960's, the late 1980's and 1990's, and particularly high values during 2005–2008. The study highlights the possible role of variability in ice export as an explanatory factor for understanding the dramatic loss of Arctic sea ice during the last decadespublishedVersio

Identifying major drivers of daily streamflow from large-scale atmospheric circulation with machine learning

Previous studies linking large-scale atmospheric circulation and river flow with traditional machine learning techniques have predominantly explored monthly, seasonal or annual streamflow modelling for applications in direct downscaling or hydrological climate-impact studies. This paper identifies major drivers of daily streamflow from large-scale atmospheric circulation using two reanalysis datasets for six catchments in Norway representing various Köppen-Geiger climate types and flood-generating processes. A nested loop of roughly pruned random forests is used for feature extraction, demonstrating the potential for automated retrieval of physically consistent and interpretable input variables. Random forest (RF), support vector machine (SVM) for regression and multilayer perceptron (MLP) neural networks are compared to multiple-linear regression to assess the role of model complexity in utilizing the identified major drivers to reconstruct streamflow. The machine learning models were trained on 31 years of aggregated atmospheric data with distinct moving windows for each catchment, reflecting catchment-specific forcing-response relationships between the atmosphere and the rivers. The results show that accuracy improves to some extent with model complexity. In all but the smallest, rainfall-driven catchment, the most complex model, MLP, gives a Nash-Sutcliffe Efficiency (NSE) ranging from 0.71 to 0.81 on testing data spanning five years. The poorer performance by all models in the smallest catchment is discussed in relation to catchment characteristics, sub-grid topography and local variability. The intra-model differences are also viewed in relation to the consistency between the automatically retrieved feature selections from the two reanalysis datasets. This study provides a benchmark for future development of deep learning models for direct downscaling from large-scale atmospheric variables to daily streamflow in Norway.publishedVersio