Recent Ventures in Interdisciplinary Arctic Research:The ARCPATH Project

This paper celebrates Professor Yongqi GAO's significant achievement in the field of interdisciplinary studies within the context of his final research project Arctic Climate Predictions: Pathways to Resilient Sustainable Societies-ARCPATH(https://www.svs.is/en/projects/finished-projects/arcpath). The disciplines represented in the project are related to climatology, anthropology, marine biology, economics, and the broad spectrum of social-ecological studies. Team members were drawn from the Nordic countries, Russia, China, the United States, and Canada. The project was transdisciplinary as well as interdisciplinary as it included collaboration with local knowledge holders. ARCPATH made significant contributions to Arctic research through an improved understanding of the mechanisms that drive climate variability in the Arctic. In tandem with this research, a combination of historical investigations and social, economic, and marine biological fieldwork was carried out for the project study areas of Iceland, Greenland, Norway, and the surrounding seas, with a focus on the joint use of ocean and sea-ice data as well as social-ecological drivers. ARCPATH was able to provide an improved framework for predicting the near-term variation of Arctic climate on spatial scales relevant to society, as well as evaluating possible related changes in socioeconomic realms. In summary, through the integration of information from several different disciplines and research approaches, ARCPATH served to create new and valuable knowledge on crucial issues, thus providing new pathways to action for Arctic communities.

A new implementation of FLEXPART with Enviro-HIRLAM meteorological input,and a case study during a heavy air pollution event

We integrated Enviro-HIRLAM(Environment-High Resolution Limited Area Model)meteorological output into FLEXPART(FLEXible PARTicle dispersion model).A FLEXPART simulation requires meteorological input from a numerical weather prediction(NWP)model.The publicly available version of FLEXPART can utilize either ECMWF(European Centre for Medium-range Weather Forecasts)Integrated Forecast System(IFS)forecast or reanalysis NWP data,or NCEP(U.S.National Center for Environmental Prediction)Global Forecast System(GFS)forecast or reanalysis NWP data.The primary benefits of using Enviro-HIRLAM are that it runs at a higher resolution and accounts for aerosol effects in meteorological fields.We compared backward trajectories gener-ated with FLEXPART using Enviro-HIRLAM(both with and without aerosol effects)to trajectories generated using NCEP GFS and ECMWF IFS meteorological inputs,for a case study of a heavy haze event which occurred in Beijing,China in November 2018.We found that results from FLEXPART were considerably different when using different meteorological inputs.When aerosol effects were included in the NWP,there was a small but noticeable differ-ence in calculated trajectories.Moreover,when looking at potential emission sensitivity instead of simply expressing trajectories as lines,additional information,which may have been missed when looking only at trajectories as lines,can be inferred.

Use of Microwave Radiances from Metop-C and Fengyun-3 C/D Satellites for a Northern European Limited-area Data Assimilation System

MetCoOp is a Nordic collaboration on operational Numerical Weather Prediction based on a common limited-area km-scale ensemble system. The initial states are produced using a 3-dimensional variational data assimilation scheme utilizing a large amount of observations from conventional in-situ measurements, weather radars, global navigation satellite system, advanced scatterometer data and satellite radiances from various satellite platforms. A version of the forecasting system which is aimed for future operations has been prepared for an enhanced assimilation of microwave radiances. This enhanced data assimilation system will use radiances from the Microwave Humidity Sounder, the Advanced Microwave Sounding Unit-A and the Micro-Wave Humidity Sounder-2 instruments on-board the Metop-C and Fengyun-3 C/D polar orbiting satellites. The implementation process includes channel selection, set-up of an adaptive bias correction procedure, and careful monitoring of data usage and quality control of observations. The benefit of the additional microwave observations in terms of data coverage and impact on analyses, as derived using the degree of freedom of signal approach, is demonstrated. A positive impact on forecast quality is shown, and the effect on the precipitation for a case study is examined. Finally, the role of enhanced data assimilation techniques and adaptions towards nowcasting are discussed.

The effects of mean sea level rise and strengthened winds on extreme sea levels in the Baltic Sea

Mean sea level rise and climatological wind speed changes occur as part of the ongoing climate change and future projections of both variables are still highly uncertain. Here the Baltic Sea’s response in extreme sea levels to perturbations in mean sea level and wind speeds is investigated in a series of simulations with a newly developed storm surge model based on the nucleus for European modeling of the ocean(NEMO)-Nordic. A simple linear model with only two tunable parameters is found to capture the changes in the return levels extremely well. The response to mean sea level rise is linear and nearly spatially uniform, meaning that a mean sea level rise of 1 m increases the return levels by a equal amount everywhere. The response to wind speed perturbations is more complicated and return levels are found to increase more where they are already high. This behaviour is alarming as it suggests that already flooding prone regions like the Gulf of Finland will be disproportionally adversely affected in a future windier climate.

Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale

Statistical analyses and descriptive characterizations are sometimes assumed to be offering information on time series forecastability.Despite the scientific interest suggested by such assumptions,the relationships between descriptive time series features(e.g.,temporal dependence,entropy,seasonality,trend and linearity features)and actual time series forecastability(quantified by issuing and assessing forecasts for the past)are scarcely studied and quantified in the literature.In this work,we aim to fill in this gap by investigating such relationships,and the way that they can be exploited for understanding hydroclimatic forecastability and its patterns.To this end,we follow a systematic framework bringing together a variety of–mostly new for hydrology–concepts and methods,including 57 descriptive features and nine seasonal time series forecasting methods(i.e.,one simple,five exponential smoothing,two state space and one automated autoregressive fractionally integrated moving average methods).We apply this framework to three global datasets originating from the larger Global Historical Climatology Network(GHCN)and Global Streamflow Indices and Metadata(GSIM)archives.As these datasets comprise over 13,000 monthly temperature,precipitation and river flow time series from several continents and hydroclimatic regimes,they allow us to provide trustable characterizations and interpretations of 12-month ahead hydroclimatic forecastability at the global scale.We first find that the exponential smoothing and state space methods for time series forecasting are rather equally efficient in identifying an upper limit of this forecastability in terms of Nash-Sutcliffe efficiency,while the simple method is shown to be mostly useful in identifying its lower limit.We then demonstrate that the assessed forecastability is strongly related to several descriptive features,including seasonality,entropy,(partial)autocorrelation,stability,(non)linearity,spikiness and heterogeneity features,among others.We further(i)show that,if such descriptive information is available for a monthly hydroclimatic time series,we can even foretell the quality of its future forecasts with a considerable degree of confidence,and(ii)rank the features according to their efficiency in explaining and foretelling forecastability.We believe that the obtained rankings are of key importance for understanding forecastability.Spatial forecastability patterns are also revealed through our experiments,with East Asia(Europe)being characterized by larger(smaller)monthly temperature time series forecastability and the Indian subcontinent(Australia)being characterized by larger(smaller)monthly precipitation time series forecastability,compared to other continental-scale regions,and less notable differences characterizing monthly river flow from continent to continent.A comprehensive interpretation of such patters through massive feature extraction and feature-based time series clustering is shown to be possible.Indeed,continental-scale regions characterized by different degrees of forecastability are also attributed to different clusters or mixtures of clusters(because of their essential differences in terms of descriptive features).

Modeling Arctic Ocean heat transport and warming episodes in the 20th century caused by the intruding Atlantic Water

This study investigates the Arctic Ocean warming episodes in the 20th century using both a high-resolution coupled global climate model and historical observations. The model, with no flux adjustment, reproduces well the Atlantic Water core temperature （AWCT） in the Arctic Ocean and shows that four largest decadalscale warming episodes occurred in the 1930s, 70s, 80s, and 90s, in agreement with the hydrographic observational data. The difference is that there was no pre-warming prior to the 1930s episode, while there were two pre-warming episodes in the 1970s and 80s prior to the 1990s, leading the 1990s into the largest and prolonged warming in the 20th century. Over the last century, the simulated heat transport via Fram Strait and the Barents Sea was estimated to be, on average, 31.32 TW and 14.82 TW, respectively, while the Bering Strait also provides 15.94 TW heat into the west- ern Arctic Ocean. Heat transport into the Arctic Ocean by the Atlantic Water via Fram Strait and the Barents Sea correlates significantly with AWCT （ C = 0.75 ） at 0- lag. The modeled North Atlantic Oscillation （NAO） index has a significant correlation with the heat transport （ C = 0.37 ）. The observed AWCT has a significant correlation with both the modeled AWCT （ C =0.49） and the heat transport （ C =0.41 ）. However, the modeled NAO index does not significantly correlate with either the observed AWCT （ C = 0.03 ） or modeled AWCT （ C = 0.16 ） at a zero-lag, indicating that the Arctic climate system is far more complex than expected.

A sensitivity study of the WRF model in offshore wind modeling over the Baltic Sea

Accurate wind modeling is important for wind resources assessment and wind power forecasting. To improve the WRF model configuration for the offshore wind modeling over the Baltic Sea, this study performed a sensitivity study of the WRF model to multiple model configurations, including domain setup,grid resolution, sea surface temperature, land surface data, and atmosphere-wave coupling. The simulated offshore wind was evaluated against LiDAR observations under different wind directions, atmospheric stabilities, and sea status. Generally, the simulated wind profiles matched observations, despite systematic underestimations. Strengthening the forcing from the reanalysis data through reducing the number of nested domains played the largest role in improving wind modeling. Atmosphere-wave coupling further improved the simulated wind, especially under the growing and mature sea conditions.Increasing the vertical resolution, and updating the sea surface temperature and the land surface information only had a slight impact, mainly visible during very stable conditions. Increasing the horizontal resolution also only had a slight impact, most visible during unstable conditions. Our study can help to improve the wind resources assessment and wind power forecasting over the Baltic Sea.

Towards an integrative approach to evaluate the environmental ecosystem services provided by urban forest

As a Nature-Based Solution,urban forests deliver a number of environmental ecosystem services(EESs).To quantify these EESs,well-defined,reliable,quantifiable and stable indicators are needed.With literature analysis and expert knowledge gathered within COST Action FP1204 GreenInUrbs,we proposed a classification of urban forest EESs into three categories:(A)regulation of air,water,soil and climate;(B)provisioning of habitat quality;and(C)provisioning of other goods and services.Each category is divided into EES types:(a)amelioration of air quality;restoration of soil and water;amelioration of the microclimate;removal of CO2 from the air;(b)provision of habitat for biodiversity;support for resilient urban ecosystems;provision of genetic diversity;and(c)provision of energy and nutrients;provision of grey infrastructure resilience.Each EES type provides one or more benefits.For each of these 12 benefits,we propose a set of indicators to be used when analyzing the impacts on the identified EESs.Around half of the 36 indicators are relevant to more than one single benefit,which highlights complex interrelationships.The indicators of wider applicability are tree and stand characteristics,followed by leaf physical traits and tree species composition.This knowledge is needed for the optimization of the EESs delivered by urban forests,now and in the future.

Site adaptation with machine learning for a Northern Europe gridded global solar irradiance product

Gridded global horizontal irradiance(GHI)databases are fundamental for analysing solar energy applications’technical and economic aspects,particularly photovoltaic applications.Today,there exist numerous gridded GHI databases whose quality has been thoroughly validated against ground-based irradiance measurements.Nonetheless,databases that generate data at latitudes above 65˚are few,and those available gridded irradiance products,which are either reanalysis or based on polar orbiters,such as ERA5,COSMO-REA6,or CM SAF CLARA-A2,generally have lower quality or a coarser time resolution than those gridded irradiance products based on geostationary satellites.Amongst the high-latitude gridded GHI databases,the STRÅNG model developed by the Swedish Meteorological and Hydrological Institute(SMHI)is likely the most accurate one,providing data across Sweden.To further enhance the product quality,the calibration technique called"site adaptation"is herein used to improve the STRÅNG dataset,which seeks to adjust a long period of low-quality gridded irradiance estimates based on a short period of high-quality irradiance measurements.This study introduces a novel approach for site adaptation of solar irradiance based on machine learning techniques,which differs from the conventional statistical methods used in previous studies.Seven machine-learning algorithms have been analysed and compared with conventional statistical approaches to identify Sweden’s most accurate algorithms for site adaptation.Solar irradiance data gathered from three weather stations of SMHI is used for training and validation.The results show that machine learning can substantially improve the STRÅNG model’s accuracy.However,due to the spatiotemporal heterogeneity in model performance,no universal machine learning model can be identified,which suggests that site adaptation is a location-dependant procedure.