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Прозрачност на водата (коефициент на дифузно затихване при 490nm, Kd490 в m^-1 при разделителна способност 9 km): Коефициентът на дифузно затихване Kd490 измерва проникването на светлина във водния стълб при синьо-зелените дължини на вълната (приблизително 490 nm). Той представлява добър показател за прозрачността на водата в резултат на комбинираното действие на поглъщане и обратно разсейване от съставките на водата и структурата на обкръжаващото светлинно поле. Transparencia del agua (coeficiente de atenuación de difusa a 490 nm, Kd490 en m^-1 a 9 km de resolución): El coeficiente de atenuación difusa Kd490 mide la penetración de luz en la columna de agua en las longitudes de onda azul-verde (aproximadamente 490 nm). Representa un buen indicador de la transparencia del agua resultante de la acción combinada de absorción y retrodispersión por los constituyentes del agua, y la estructura del campo de luz circundante. Trasparenza tal-Ilma (koeffiċjent ta’ attenwazzjoni diffuż f’490nm, Kd490 f’m^-1 b’riżoluzzjoni ta’ 9 km): Il-koeffiċjent tal-attenwazzjoni diffuża Kd490 ikejjel il-penetrazzjoni tad-dawl fil-kolonna tal-ilma fit-tul ta’ mewġ blu-aħdar (madwar 490 nm). Dan jirrappreżenta indikatur tajjeb tat-trasparenza tal-ilma li jirriżulta mill-azzjoni kkombinata tal-assorbiment u r-retrodiffużjoni mill-kostitwenti tal-ilma, u l-istruttura tal-qasam tad-dawl tal-madwar. Trasparenza dell'acqua (coefficiente di attenuazione differenziale a 490nm, Kd490 in m^-1 a risoluzione di 9 km): Il coefficiente di attenuazione diffuso Kd490 misura la penetrazione della luce nella colonna d'acqua alle lunghezze d'onda blu-verde (ca. 490 nm). Rappresenta un buon indicatore di trasparenza dell'acqua derivante dall'azione combinata di assorbimento e retrodiffusione dai costituenti dell'acqua e dalla struttura del campo di luce circostante. Transparence de l’eau (coefficient d’atténuation diffuse à 490nm, Kd490 en m^-1 à résolution de 9 km): Le coefficient d’atténuation diffuse Kd490 mesure la pénétration de la lumière dans la colonne d’eau aux longueurs d’onde bleu-vert (environ 490 nm). Il représente un bon indicateur de transparence de l’eau résultant de l’action combinée d’absorption et de rétrodiffusion par les constituants de l’eau, et de la structure du champ lumineux environnant. Wassertransparenz (Diffuse-Dämpfungskoeffizient bei 490nm, Kd490 in m^-1 bei 9 km Auflösung): Der diffuse Dämpfungskoeffizient Kd490 misst die Lichtdurchdringung in der Wassersäule bei den blau-grünen Wellenlängen (ca. 490 nm). Es stellt einen guten Indikator für die Wassertransparenz dar, der sich aus der kombinierten Wirkung von Absorption und Rückstreuung durch die Wasserbestandteile und der Struktur des umgebenden Lichtfeldes ergibt. Transparența apei (coeficientul de atenuare a difuzării la 490nm, Kd490 în m^-1 la o rezoluție de 9 km): Coeficientul de atenuare difuză Kd490 măsoară pătrunderea luminii în coloana de apă la lungimile de undă albastru-verde (aproximativ 490 nm). Acesta reprezintă un bun indicator al transparenței apei care rezultă din acțiunea combinată de absorbție și backscattering de către constituenții de apă și structura câmpului luminos din jur. Διαφάνεια στο νερό (διάχυτος συντελεστής εξασθένησης στα 490nm, Kd490 σε m^-1 σε ανάλυση 9 km): Ο διάχυτος συντελεστής εξασθένισης Kd490 μετρά τη διείσδυση του φωτός στη στήλη νερού στα γαλαζοπράσινα μήκη κύματος (περίπου 490 nm). Αντιπροσωπεύει έναν καλό δείκτη της διαφάνειας του νερού που προκύπτει από τη συνδυασμένη δράση της απορρόφησης και της οπισθοσκέδασης από τα συστατικά του νερού, και τη δομή του γύρω φωτεινού πεδίου. Water Transparency (Diffuse attenuation coefficient at 490nm, Kd490 in m^-1 at 9km resolution): The diffuse attenuation coefficient Kd490 measures the light penetration in the water column at the blue-green wavelengths (ca. 490 nm). It represents a good indicator of water transparency resulting from the combined action of absorption and backscattering by the water constituents, and the structure of the surrounding light field. Przejrzystość wody (współczynnik tłumienia rozproszonego przy 490 nm, Kd490 w m^-1 przy rozdzielczości 9 km): Współczynnik tłumienia rozproszonego Kd490 mierzy przenikanie światła w słupie wody na niebiesko-zielonych długościach fali (ok. 490 nm). Stanowi dobry wskaźnik przejrzystości wody wynikającej z połączonego działania absorpcji i rozpraszania wstecznego przez składniki wody oraz struktury otaczającego pola światła.

Ekvivalentinės juodosios anglies matavimai Isproje, Italijoje. Măsurători ale carbonului negru echivalent în Ispra, Italia. Вимірювання еквівалентного чорного вуглецю в Іспрі, Італія. Измервания на еквивалентен черен въглерод в Испра, Италия. Merania ekvivalentného čierneho uhlíka v Ispre, Taliansko. Tomhais de charbón dubh coibhéiseach in Ispra na hIodáile. Metingen van equivalente zwarte koolstof in Ispra, Italië. Mediciones de carbono negro equivalente en Ispra, Italia. Measurements of equivalent black carbon in Ispra, Italy. Pomiary równoważnego czarnego węgla w Ispra we Włoszech.

Climate and vegetation change in a coastal marsh: two snapshots of groundwater dynamics and tidal flooding at Piermont Marsh, NY spanning 20 years We include water levels measured along a transect of groundwater wells in 1999 and 2019, statistical analyses of ground water data, tidal efficiency estimates, vegetation data from 1997, 2005, 2014, and 2018, measures of tide gauge data and sea level rise from the Battery, New York Harbor. We attach the following three groups of files: (1) Files related to data from Piermont Marsh, which includes water levels in wells, tide gauge data collected from the tidal channel, and vegetation data; (2) Files related to analysis of water levels at Piermont Marsh; (3) Files related to analysis of Battery tide gauge data, Battery tide predictions, and precipitation data ## Description of the data and file structure **(1) Files related to data from Piermont Marsh, which includes water levels in wells, tide gauge data collected from the tidal channel, and vegetation data** 1999PiermontWaterlevels.csv 2019PiermontWaterLevels.csv channel_1999.xls channel_2019.xls water_level_elevations.csv Vegetation.xls 1999PiermontWaterlevels.csv and 2019PiermontWaterLevels.csv - Water levels collected at Piermont marsh in groundwater wells, at 0-m, 6-m, 12-m, 18-m, 24-m, 36-m, and 48-m from a tidal channel. The files contain three fields: daytime, well, and elevation. The daytime is the date and time the water level was collected, hours in Eastern Daylight Time -4GMT. The well number refers to its location relative to the tidal channel, with #1 referring to 0-m, #2 referring to 6-m, #3 referring to 12-m, #4 referring to 18-m, #5 referring to 24-m, #6 referring to 36-m, and #7 referring to 48-m. The elevation field refers to the water level in meters relative to the NAVD88 datum. In 1999 water levels were collected 14 April 2019 - 26 May 2019. In 2019, water levels were collected 5 May 2019 - 30 June 2019. channel_1999.xls - This file shows the elevation of water level in the channel. There is a field for date and time, in GMT -4, and water level in meters relative to NGVD29. channel_2019.xls - This file shows the elevation of water level in the channel. There is a field for Date, Time, in GMT -4, absolute pressure in in mbar, temperature in degrees C, and water level in meters relative to NAVD88. water_level_elevations.csv - This csv file includes five fields. The first is "year" or the year collected (1999 or 2019). The second is "well" numbered 1-7. Well 1 is closest to the channel while 7 is the furthest from the channel. #1 referrs to 0-m from the channel, #2 referring to 6-m from the channel, #3 referring to 12-m from the channel, #4 referring to 18-m from the channel, #5 referring to 24-m from the channel, #6 referring to 36-m from the channel, and #7 referring to 48-m from the channel. The datetime field refers to the day and time the measure was made in day/month/year HH:MM AM/PM format. The next field is lunarcyle which refers to whether the measure was made during "spring" or "neap" tidal cycles. Spring was assigned to the tides the week of full or new moons, Neap was assigned to tides the week of the first and last quarter. The last is "elevation" and is the measure of water levels in meters relative to the NAVD88 datum. Vegetation.xls - This Excel file includes four sheets that each refer to a year of vegetation date - 1997, 2005, 2014, and 2017. The first field is "well" which has a number 1 through 7. The well number refers to its location relative to the tidal channel, with #1 referring to 0-m, #2 referring to 6-m, #3 referring to 12-m, #4 referring to 18-m, #5 referring to 24-m, #6 referring to 36-m, and #7 referring to 48-m. There is a field for latitude (lat) and longitude (long), which refers to the location of the shape in UTM, in meters, in the 18N. Cover refers to the plant cover type, area is the area of the polygon in square meters. **(2) Files related to analysis of water levels at Piermont Marsh** Distancefromsurface.R MinNeap_MarshSurface.csv MaxNeap_MarshSurface.csv MinSpring_MarshSurface.csv MaxSpring_MarshSurface.csv PiermontEfficiencyRggplot.csv Tidalefficiency.R The R file Distancefromsurface.R includes calculations of mean and variance of water levels, and as well as production of relevant figures. MinNeap_MarshSurface.csv file has low tide minimum water levels during neap tides (weeks centered on the moons first and third quarter). It includes the following fields: distance, year, water_elevation, marsh_elevation, and distance_surface. The field distance, is distance from the tidal channel, in meters. The field year, refers to is the year collected (1999 or 2019). The field water_elevation, is the elevation of the water level at low tide, in meters relative to the NGVD88 datum. The field marsh_elevation refers to the height of the marsh at that location, in meters relative to the NGVD88 datum. The field distance_surface is the difference between the marsh elevation and the water elevation. Positive values are values below the marsh surface, while negative values are values above the marsh surface. MaxNeap_MarshSurface.csv file has high tide maximum water levels during neap tides (weeks centered on the moons first and third quarter). It includes the following fields: distance, year, water_elevation, marsh_elevation, and distance_surface. The field distance, is distance from the tidal channel, in meters. The field year, refers to is the year collected (1999 or 2019). The field water_elevation, is the elevation of the water level at high tide, in meters relative to the NGVD88 datum. The field marsh_elevation refers to the height of the marsh at that location, in meters relative to the NGVD88 datum. The field distance_surface is the difference between the marsh elevation and the water elevation. Positive values are values below the marsh surface, while negative values are values above the marsh surface. MinSpring_MarshSurface.csv file has low tide minimum water levels during spring tides (weeks centered on the new and full moon). It includes the following fields: distance, year, water_elevation, marsh_elevation, and distance_surface. The field distance, is distance from the tidal channel, in meters. The field year, refers to is the year collected (1999 or 2019). The field water_elevation, is the elevation of the water level at low tide, in meters relative to the NGVD88 datum. The field marsh_elevation refers to the height of the marsh at that location, in meters relative to the NGVD88 datum. The field distance_surface is the difference between the marsh elevation and the water elevation. Positive values are values below the marsh surface, while negative values are values above the marsh surface. MaxSpring_MarshSurface.csv file has high tide maximum water levels during spring tides (weeks centered on the new and full moon). It includes the following fields: distance, year, water_elevation, marsh_elevation, and distance_surface. The field distance, is distance from the tidal channel, in meters. The field year, refers to is the year collected (1999 or 2019). The field water_elevation, is the elevation of the water level at high tide, in meters relative to the NGVD88 datum. The field marsh_elevation refers to the height of the marsh at that location, in meters relative to the NGVD88 datum. The field distance_surface is the difference between the marsh elevation and the water elevation. Positive values are values below the marsh surface, while negative values are values above the marsh surface. PiermontEfficiencyRggplot.csv - file lists the well number (1-7), distance (a number 1-14, which gives a unique identifier to each combination of well and year), year, which was the year the data was collected. The last field is efficiency. This field refers to the ratio between the change in water level over the course of a tidal cycle in the well to the change in the water level over the course of the tidal cycle at the Battery tide gauge, NYC. Tidalefficiency.R - file that plots and calculates tidal efficiency during 1999 and 2019 at each well. **(3) Files related to analysis of Battery tide gauge data, Battery tide predictions, and precipitation data** MSL_time.R 3348871.csv 3348873.csv Battery.csv Bat_wls.csv monthly.csv sin2.csv predictions.csv tide_l.csv wls.csv MSL_time.R - This R code uses several data files to conduct analysis of change over time in water levels and monthly anomalies in precipitation and water levels. All necessary packages are described. 3348871.csv and 3348873.csv - are weather data from Westchester County airport, station USW00094745 from 1997 to 2001 (3348873.csv) 2017 to 2022 (3348871.csv). The field station lists the station. The field Name is the name of the station, Westchester County Airport. The date is the day data was collected. AWND refers to Average daily wind speed in miles per hour. PGTM refers to peak gust time (hours and minutes, i.e., HHMM). PRCP refers to precipitation in inches, TMAX refers to the maximum daily temperature, in degrees Fahrenheit. TMIN refers to the minimum daily temperature, in degrees Fahrenheit. WDF2 is the direction of fastest 2-minute wind in degrees. WDF5 is the direction of fastest 5-second wind in degrees. WSF2 is the fastest 2-minute wind speed in miles per hour. WSF5 is the fastest 5-second wind speed in miles per hour. Missing data is replaced with -999. Battery.csv - all high tide levels for 1997 through 2022. The two fields are level, referring to high tide water levels in meters relative to the NAVD88 datum. The second field is year. Bat_wls.csv is monthly tide levels from the Battery tide gauge, NY. The year field refers to year including fraction. Mean high water (MHW) refers to monthly mean high water relative to the NAVD88 datum in meters. Mean sea level (MSL) refers to monthly mean sea level relative to the NAVD88 datum in meters. Mean tide level (MTL) refers to monthly mean tide level relative to the NAVD88 datum in meters.. Mean Low Water (MLW) refers to monthly mean low water relative to the NAVD88 datum in meters. monthly.csv - is mean high water and mean sea level from 1980-2022, by month. The field month refers to the month (January =1). MHW is monthly mean high water for all months, relative to the NAVD88 datum, and MSL is monthly mean sea level relative to the NAVD88 datum. sin2.csv is the monthly mean sea level at the Battery tide gauge (1980-2022), with a 1 year rolling window median smooth added. There are three fields, month, MSL, and year. Month is the number of months elapsed since January 1961. MSL is the monthly mean sea level in meters, relative to the NAVD88 datum, with a one year smoothing function applied. Year refers to the observation month, expressed in years and the fraction of years so January 1980 would be 1980, while February 1980 is depicted as 1980.083. predictions.csv - tide predictions for the Battery tide gauge, New York City. Fields are y, which stands for year, represented by year, including fractions representing months. High_p is the highest predicted tide of the month, in meters relative to the NAVD88 datum. MHW_p is the predicted mean high tide for the month relative to the NAVD88 datum. MLW_p is the predicted mean low tide for the month relative to the NAVD88 datum. MTL_p is the predicted mean tide level for the month relative to the NAVD88 datum. High_1 is the highest actual tide of the month, in meters relative to the NAVD88 datum. MHW_a is the actual mean high tide for the month relative to the NAVD88 datum. MLW_a is the actual mean low tide for the month relative to the NAVD88 datum. MTL_a is the actual mean tide level for the month relative to the NAVD88 datum. tide_l.csv is a file with the monthly mean high water (MHW_l), monthly mean tide level (MTL_l), and mean low water (MLW_l) for 1960 -2021. wls.csv is a file that has monthly water levels from 1999 to 2019, listing year (as a fraction, not just an integer for month), Highest, as the highest tide of the month in meters relative to the NAVD88 datum. MHW refers to the mean high water during the month in meters relative to the NAVD88 datum. MTL refers to the mean tidal level during the month in meters relative to the NAVD88 datum. MLW refers to the mean low water during the month in meters relative to the NAVD88 datum. ## Sharing/Access information Data was derived from the following external sources: * Vegetation shapefiles for the Hudson River NERR for 1997, 2005, and 2014, were obtained through personal request to Sarah Fernald, *Reserve Manager and Research Coordinator.* Files should be available through the Reserve website, although the link is not functional at this time: * The 2018 vegetation shapefiles were obtained from under the heading, [Hudson River Estuary tidal wetlands](https://data.gis.ny.gov/datasets/ee2723393f894e929dbd6dbdc84770de_0/explore?location=41.308770%2C-73.842410%2C9.10). * We acknowledge the NYS DEC Hudson River Estuary Program, NYS DEC Hudson River National Estuarine Research Reserve, and Cornell Institute for Resource Information Sciences for collection and curation of the Hudson River NERR vegetation data. * Tide gauge data and tide predictions for the Battery, NY were obtained from NOAA tides and currents website: * Precipitation data was obtained from the National Centers for Environmental Information, NOAA: . The station for which data was obtained was the Westchester County airport, station USW00094745. ## Code/Software We provide three R files, which we ran using R version 4.3.1 (2023-06-16), in R Studio 2022.02.1, Build 461. All required packages are described in the .R files. Distancefromsurface.R - This R code utilizes four data files that include low tides during spring tides, low tides during neap tides, high tides during spring tides, and high tides during neap files to compare average and variance in low and high tide water levels during 1999 and 2019 relative to the marsh surface and relative to the NAVD88 datum. Code is also included to produce plots. Tidalefficiency.R - file that plots and calculates tidal efficiency during 1999 and 2019 at each well. MSL_time.R - This R code uses several data files to conduct analysis of change over time in water levels and monthly anomalies in precipitation and water levels. Hydrological measurements were collected during the spring and summer of 1999 and 2019 in Piermont Marsh (coordinates 41.0361°, -73.9105°). These measurements covered a transect that was laid out perpendicular to a tidal channel. The objective of this study was to compare the current tidal flooding and groundwater table levels with the data from 1999. The goal was to assess the differences in tidal hydrology between these two distinct time periods, which also differed in terms of marsh and water level elevations. To determine groundwater levels and tidal flooding across the marsh, we installed seven water level loggers along a gradient, ranging from the tidal channel to the upland area. We constructed wells by suspending pressure transducers within 7.5 cm diameter perforated PVC pipes lined with screening to prevent sediment from entering the well. These wells were positioned one meter below the marsh surface, 0.6 meters above the soil surface, vented to the atmosphere, and only the section below the soil surface was perforated. Additionally, we installed concrete collars at the marsh surface around the wells to prevent preferential water flow down the well sides. These seven wells were placed along the original transect, perpendicular to the creek, with increasing distances (0 meters, 6 meters, 12 meters, 18 meters, 24 meters, 36 meters, and 48 meters). We installed and monitored the wells from May 5 to June 30, 2019, and from April 6 to May 26, 1999. In 2019, we measured the absolute elevation of the top of each well using RTK-enabled static GPS measurements from Leica GNSS GS14 rover units and static measurements with an AX1202 GG base station unit to reference water levels to the NAVD88 vertical datum. We measured reference water levels each time data was collected, which involved determining the distance from the top of the well to the water surface and converting it to elevation relative to the NAVD88 datum. To relate marsh elevation to water elevations, GPS surveys were conducted along the transect using a Leica GNSS GS14 rover unit. In 1999, elevation control for the wells and water levels was similarly measured using survey-grade GPS. We compared changes in the marsh water table with significant potential hydrological and vegetation changes that have occurred over the past 20 years. We calculated the rates of change in monthly water levels at Battery, NY for the period from 1999 to 2019 using two different methods. We modeled changes over time in monthly highest water levels, mean high water (MHW), mean tide level (MTL), and mean low water (MLW) using an ordinary least squares regression model with ARIMA errors to account for the autoregressive structure of tide data. We removed the annual cycle first using a curve with a 1-year periodicity. The ARIMA errors model was fitted using the "auto.arima" function from the "forecast" package. We calculated the squared correlation of fitted values to actual values to produce a pseudo-r2. For comparison, we calculated trends using ordinary least squares regression for the 1999-2019 period, although it's important to note that the temporal autocorrelation likely results in underestimated uncertainty. We obtained vegetation maps from the HRNERR for 1997, 2005, 2014, and 2018 to help assess changes in the coverage of plant species over time, as these changes could impact evapotranspiration and water table patterns. A 20-meter buffer zone was created around each well location, and the composition of vegetation within this buffer zone was quantified using QGIS version 3.30.2. While four time-points may not be sufficient for statistically identifying trends, we analyzed the changes observed. To put the measurement time periods in context and ensure that our selected seasons were not anomalous, we compared water levels in spring 1999 and 2019 relative to the astronomical cycles driving interannual sea level variability using data from the Battery tide gauge. We also compared spring high tide levels in 1999 and 2019 with surrounding years. The main astronomical cycles thought to influence tides include the 18.6-year lunar nodal cycle and the 4.4-year subharmonic of the 8.85-year lunar perigee cycle. As our 1999 and 2019 measurements were collected during slightly different time periods (April/May 1999 vs. May/June 2019), we also examined mean monthly water levels (1980-2022) from the NOAA Battery tidal gauge to identify potential artifacts. We obtained rainfall data from spring 1999 and 2019 from the nearest precipitation monitoring station (Westchester airport) to determine whether the measurements were made during an unusually wet or dry period. The sampling periods were 20 years apart, so they occurred at approximately the same point in the 18.6-year lunar nodal cycle. Pressure transducer data was processed using HOBOware Pro (Version 3.7.16, Onset Computer Corporation, Bourne, MA) with reference water levels collected in the field. The data were corrected for atmospheric pressure using the HOBOware barometric compensation assistant, using data from the Hudson River National Estuarine Research Reserve. Raw water elevation data from 1999 was analyzed in conjunction with the 2019 data. Water level data from 1999 were converted from the NVGD29 to NAVD 88 datum using NOAA VDatum v4.0.1 prior to analysis. Well seven's transducer experienced three brief malfunctions from May 30 to June 3, 2019, resulting in inaccurate elevation measurements for a total of 19.5 hours. These data were excluded from the analysis. In 1999, well seven also experienced malfunctions, which were corrected by Montalto into smoothed six-hour increments using average water elevation measurements and calculated error, calibrated using regression. No other well transducers appeared to have malfunctioned. Groundwater hydrology plays an important role in coastal marsh biogeochemical function, in part because groundwater dynamics drive the zonation of macrophyte community distribution. Changes that occur over time, such as sea level rise and shifts in habitat structure are likely altering groundwater dynamics and eco-hydrological zonation. We examined tidal flooding and marsh water table dynamics in 1999 and 2019 and mapped shifts in plant distributions over time, at Piermont Marsh, a brackish tidal marsh located along the Hudson River Estuary near New York City. We found evidence that the marsh surface was flooded more frequently in 2019 than in 1999, and that tides were propagating further into the marsh in 2019, although marsh surface elevation gains were largely matching that of sea level rise. The changes in groundwater hydrology that we observed are likely due to the high tide rising at a rate that is greater than that of mean sea level. In addition, we reported on changes in plant cover by P. australis, which has displaced native marsh vegetation at Piermont Marsh. Although P. australis has increased in cover, wrack deposition and plant die off associated Superstorm Sandy allowed for native vegetation to rebound in part of our focus area. These results suggest that climate change and plant community composition may interact to shape ecohydrologic zonation. Considering these results, we recommend that habitat models consider tidal range expansion and groundwater hydrology as metrics when predicting the impact of sea level rise on marsh resilience.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.MIROC.MIROC6.ssp119' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MIROC6 climate model, released in 2017, includes the following components: aerosol: SPRINTARS6.0, atmos: CCSR AGCM (T85; 256 x 128 longitude/latitude; 81 levels; top level 0.004 hPa), land: MATSIRO6.0, ocean: COCO4.9 (tripolar primarily 1deg; 360 x 256 longitude/latitude; 63 levels; top grid cell 0-2 m), seaIce: COCO4.9. The model was run by the JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan) (MIROC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

Log and boot files of game 261

Whether old-growth (OG) forests have higher genetic diversity and effective population size, consequently higher conservation value and climate adaptive potential than second-growth (SG) forests, remain an unresolved issue. We have tested the hypothesis that old-growth forest tree populations have higher genetic diversity, effective population size (N E ), climate adaptive potential and conservation value and lower genetic differentiation than second-growth forest tree populations, employing a keystone and long-lived conifer, eastern white pine (EWP; Pinus strobus). Genetic diversity and population structure of old-growth and second-growth populations of eastern white pine (EWP) were examined using microsatellites of the nuclear and chloroplast genomes and single nucleotide polymorphisms (SNPs) in candidate nuclear genes putatively involved in adaptive responses to climate and underlying multilocus genetic architecture of local adaptation to climate in EWP. Old-growth and second-growth EWP populations had statistically similar genetic diversity, inbreeding coefficient and inter-population genetic differentiation based on nuclear microsatellites (nSSRs) and SNPs. However, old-growth populations had significantly higher chloroplast microsatellites (cpSSRs) haploid diversity than second-growth populations. Old-growth EWP populations had significantly higher coalescence-based historical long-term N E than second-growth EWP populations, but the linkage disequilibrium (LD)-based contemporary N E estimates were statistically similar between the old-growth and second-growth EWP populations. Analyses of population genetic structure and inter-population genetic relationships revealed some genetic constitution differences between the old-growth and second-growth EWP populations. Overall, our results suggest that old-growth and second-growth EWP populations have similar genetic resource conservation value. Because old-growth and second-growth EWP populations have similar levels of genetic diversity in genes putatively ...

Resultados interactivos de una investigación llevada a cabo sobre la implementación de estrategias de rehabilitación optimizadas aplicadas al parque residencial social del sur de España en diferentes zonas climáticas (A3, A4, B4 y C3), ante escenarios climáticos futuros de cambio climático. En concreto, se ha realizado un análisis multiobjetivo en el que se optimizan, a partir de la aplicación de algoritmos genéticos, los costes de intervención de diversas soluciones de rehabilitación y el confort térmico interior en verano e invierno de las viviendas sociales, bajo escenarios futuros de calentamiento global. Todo ello se realiza mediante modelos parametrizados y validados a nivel de conjunto edificatorio, implementando información real contenida en una base de datos facilitada por la Agencia de Vivienda y Rehabilitación de Andalucía (AVRA) en los modelos de simulación dinámica de conjunto. Los resultados de esta investigación están vinculados con el proyecto: Optimización Paramétrica de Fachadas de Doble Piel en Clima Mediterráneo para la Mejora de la Eficiencia Energética ante Escenarios de cambio Climático (BIA2017-86383-R). La visualización de los resultados de esta investigación se realiza a través de ficheros .html que pueden ser accedidos fácilmente mediante cualquier navegador web. Existen tres tipos de figuras interactivas: gráficas de dispersión en 3d, gráficas de dispersión en 2d y gráficas de ejes paralelos. Se ha generado por cada zona climática analizada estos tres tipos de gráficos. En el caso de los gráficos de dispersión en 3d, el entorno web permite girar y aumentar la figura, para facilitar su visualización en el espacio. Además, colocando el cursor sobre cada punto, pueden consultarse los valores específicos de las variables de optimización (porcentaje de horas con temperaturas por encima del límite superior e inferior del confort y costes de inversión en €/m2 construido). En los gráficos en dispersión en 2d, colocando el cursor sobre cada punto, se despliega una ventana en la que pueden visualizarse los diferentes valores asociados a ese punto. En lo referente a las figuras de coordenadas paralelas, las variables de optimización pueden filtrase, seleccionando y arrastrando el cursor sobre un rango de valores buscado. Lo mismo puede realizarse con el resto de variables combinatorias. Hecho esto, la herramienta web mostrará la combinación de los paquetes de rehabilitación óptimos (variables de rehabilitación ligadas a la mejora energética de la envolvente térmica y las variables operacionales analizadas). Cada combinación, tendrá asociado un valor concreto de horas fuera del confort en verano e invierno, así como de costes de inversión. Por consiguiente, es posible realizar una comparación rápida y genérica entre diferentes actuaciones y seleccionar, de forma acorde, valorando los resultados, las medidas de rehabilitación que mejor se ajusten a los Programas e Iniciativas rehabilitadoras consideradas. v.1

Fortuna station (Centro de Investigaciones Jorge L. Arauz)TowerLocation: 8�� 43.340'N, 82�� 14.241'WSolar Radiation, Pyranometer, Interval max/min/avgLocated in the highlands of the Chiriqui Province, in western Panama.There are three sensor locations: north clearing, south clearing, and a 15m tower.