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Permafrost is perennially frozen ground, such as soil, rock, and ice. In permafrost regions, plant and microbial life persists primarily in the near-surface soil that thaws every summer, called the ‘active layer’ (Figure 20). The cold and wet conditions in many permafrost regions limit decomposition of organic matter. In combination with soil mixing processes caused by repeated freezing and thawing, this has led to the accumulation of large stocks of soil organic carbon in the permafrost zone over multi-millennial timescales. As the climate warms, permafrost carbon could be highly vulnerable to climatic warming. Permafrost occurs primarily in high latitudes (e.g. Arctic and Antarctic) and at high elevation (e.g. Tibetan Plateau, Figure 21). The thickness of permafrost varies from less than 1 m (in boreal peatlands) to more than 1 500 m (in Yakutia). The coldest permafrost is found in the Transantarctic Mountains in Antarctica (−36°C) and in northern Canada for the Northern Hemisphere (-15°C; Obu et al., 2019, 2020). In contrast, some of the warmest permafrost occurs in peatlands in areas with mean air temperatures above 0°C. Here permafrost exists because thick peat layers insulate the ground during the summer. Most of the permafrost existing today formed during cold glacials (e.g. before 12 000 years ago) and has persisted through warmer interglacials. Some shallow permafrost (max 30–70m depth) formed during the Holocene (past 5000 years) and some even during the Little Ice Age from 400–150 years ago. There are few extensive regions suitable for row crop agriculture in the permafrost zone. Additionally, in areas where large-scale agriculture has been conducted, ground destabilization has been common. Surface disturbance such as plowing or trampling of vegetation can alter the thermal regime of the soil, potentially triggering surface subsidence or abrupt collapse. This may influence soil hydrology, nutrient cycling, and organic matter storage. These changes often have acute and negative consequences for continued agricultural use of such landscapes. Thus, row-crop agriculture could have a negative impact on permafrost (e.g. Grünzweig et al., 2014). Conversely, animal husbandry is widespread in the permafrost zone, including horses, cattle, and reindeer.

The speciation of dissolved iron (DFe) in the ocean is widely assumed to consist almost exclusively of Fe(III)-ligand complexes. Yet in most aqueous environments a poorly defined fraction of DFe also exists as Fe(II), the speciation of which is uncertain. Here we deploy flow injection analysis to measure in situ Fe(II) concentrations during a series of mesocosm/microcosm/multistressor experiments in coastal environments in addition to the decay rate of this Fe(II) when moved into the dark. During five mesocosm/microcosm/multistressor experiments in Svalbard and Patagonia, where dissolved (0.2 µm) Fe and Fe(II) were quantified simultaneously, Fe(II) constituted 24 %–65 % of DFe, suggesting that Fe(II) was a large fraction of the DFe pool. When this Fe(II) was allowed to decay in the dark, the vast majority of measured oxidation rate constants were less than calculated constants derived from ambient temperature, salinity, pH, and dissolved O2. The oxidation rates of Fe(II) spikes added to Atlantic seawater more closely matched calculated rate constants. The difference between observed and theoretical decay rates in Svalbard and Patagonia was most pronounced at Fe(II) concentrations <2 nM, suggesting that the effect may have arisen from organic Fe(II) ligands. This apparent enhancement of Fe(II) stability under post-bloom conditions and the existence of such a high fraction of DFe as Fe(II) challenge the assumption that DFe speciation in coastal seawater is dominated by ligand bound-Fe(III) species.

From 2003 to 2013, the Ancona section of CNR-IRBIM (formerly part of CNR-Institute of Marine Science) runned the "Fishery Observing System" (FOS) program aimed at using Italian fishing vessels as Vessels Of Opportunity (VOOs) for the collection of scientifically useful datasets (Falco et al. 2007). Some commercial fishing vessels, targetting small pelagic species in the northern and central Adriatic Sea, were equipped with an integrated system for the collection of information on catches, position of the fishing operation, depth and water temperature during the haul, producing a great amount of data that demonstrated to be helpful both for oceanographic and fishery biology purposes (Carpi et al. 2015; Aydo?du et a. 2016; Sparnocchia et al. 2016; Lucchetti et al. 2018). In 2012, thanks to the participation to some national and international projects (e.g. SSD-Pesca, EU-FP7 JERICO etc.), CNR started the development of a new modular "Fishery & Oceanography Observing System" (FOOS; Patti et al. 2013). New sensors for oceanographic and meteorological data allow nowadays the FOOS to collect more parameters, with higher accuracy and to send them directly to a data center in near real time (Martinelli et al. 2016; Sparnocchia et al. 2017). Furthermore, the FOOS is a multifunction system able to collect various kind of data from the fishing operations and also to send back to the fishermen useful information (e.g. weather and sea forecasts, etc.) through an electronic logbook with an ad hoc software embedded. The new FOOS installed on various kind of fishing vessels targetting different resources, allowed a spatial extension of the monitored areas in the Mediterranean Sea (Patti et al. 2013). CNR-IRBIM implemented the "AdriFOOS" observational system, by installing the FOOS on some commercial fishing boats operating in the Adriatic Sea. Since then the datacenter based in Ancona receives daily data sets of environmental parameters collected along the water column and close to the sea bottom (eg. temperature, salinity, etc.), together with GPS haul tracks, catch amounts per haul, target species sizes and weather information. Some temperature and salinity measurements acquired by the FOOS in the Adriatic Sea from January 2014 to March 2015 were published within the JERICO project and some oxygen and fluorescence profiles obtained in 2017 within the NEXOS project. The dataset here presented contains 14803 depth/temperature profiles collected by 10 vessels of the AdriFOOS fleet in the period 2012-2020. All the profiles were subjected to quality control.Data are flagged according the L20 (SEADATANET MEASURAND QUALIFIER FLAGS).

This pocket guide is a product of the EU Horizon 2020 project INTERACT II - grant number 871120. It shows some basic information how to manage data within a scientific project which involves field work.

An equilibrium simulation of Marine Isotope Stage 3 (MIS3) climate with boundary conditions characteristic of Greenland Interstadial 8 (GI-8; 38 kyr BP) is carried out with the Norwegian Earth System Model (NorESM). A computationally efficient configuration of the model enables long integrations at relatively high resolution, with the simulations reaching a quasi-equilibrium state after 2500 years. We assess the characteristics of the simulated large-scale atmosphere and ocean circulation, precipitation, ocean hydrography, sea ice distribution, and internal variability. The simulated MIS3 interstadial near-surface air temperature is 2.9 ∘C cooler than the pre-industrial (PI). The Atlantic meridional overturning circulation (AMOC) is deeper and intensified by ∼13 %. There is a decrease in the volume of Antarctic Bottom Water (AABW) reaching the Atlantic. At the same time, there is an increase in ventilation of the Southern Ocean, associated with a significant expansion of Antarctic sea ice and concomitant intensified brine rejection, invigorating ocean convection. In the central Arctic, sea ice is ∼2 m thicker, with an expansion of sea ice in the Nordic Seas during winter. Attempts at triggering a non-linear transition to a cold stadial climate state, by varying atmospheric CO2 concentrations and Laurentide Ice Sheet height, suggest that the simulated MIS3 interstadial state in the NorESM is relatively stable, thus underscoring the role of model dependency, and questioning the existence of unforced abrupt transitions in Greenland climate in the absence of interactive ice sheet–meltwater dynamics.

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A detailed survey of a high Arctic fjord (Kongsfjorden, Svalbard), subjected to a large glacier discharge, was carried out from 24 July to 13 August 2016. Field activities addressed the identification of the effects of glacier and iceberg melting on the evolution of nutrient, dissolved organic matter and carbonate systems in this coastal marine environment. Hydrological (CTD downcasts) and biogeochemical (bottle sampling) data were collected during six oceanographic surveys in the inner area of the fjord, in concomitance to the annual phase of maximum air warming. An extensive sampling was also carried out in all glacier drainage systems located around the fjord and from several iceberg samples, in order to characterize all freshwater loads. The dataset includes hydrological data (T, Sal., density) carbonate chemistry data (pH, DIC, TA) and the concentrations of dissolved oxygen (DO), inorganic nutrients (NO3-, NO2-, NH4+, PO43-, SiO2), dissolved organic matter (DOC, DON) and some micronutrients (Fe, Mn).

At every station, pressure (P), salinity (S), potential temperature (?) dissolved oxygen concentration (DO) and Fluorescence have been acquired and are part of the database

time series has been collected by the Italian Consiglio Nazionale delle Ricerche (CNR) in a station located at about 3500 m depth in the central Tyrrhenian Sea (39° 46.85 N, 011° 53.00 E) over the period 2003-2016. The dataset contains 21 hydrological profiles (Pressure (dbar), Temperature (ITS-90, °C) and Salinity) performed on average every six months, with a lack of data in the period 2007-2010.

We present a mapped climatology (GLODAPv2.2016b) of ocean biogeochemical variables based on the new GLODAP version 2 data product (Olsen et al., 2016; Key et al., 2015), which covers all ocean basins over the years 1972 to 2013. The quality-controlled and internally consistent GLODAPv2 was used to create global 1° × 1° mapped climatologies of salinity, temperature, oxygen, nitrate, phosphate, silicate, total dissolved inorganic carbon (TCO2), total alkalinity (TAlk), pH, and CaCO3 saturation states using the Data-Interpolating Variational Analysis (DIVA) mapping method. Improving on maps based on an earlier but similar dataset, GLODAPv1.1, this climatology also covers the Arctic Ocean. Climatologies were created for 33 standard depth surfaces. The conceivably confounding temporal trends in TCO2 and pH due to anthropogenic influence were removed prior to mapping by normalizing these data to the year 2002 using first-order calculations of anthropogenic carbon accumulation rates. We additionally provide maps of accumulated anthropogenic carbon in the year 2002 and of preindustrial TCO2. For all parameters, all data from the full 1972–2013 period were used, including data that did not receive full secondary quality control. The GLODAPv2.2016b global 1° × 1° mapped climatologies, including error fields and ancillary information, are available at the GLODAPv2 web page at the Carbon Dioxide Information Analysis Center (CDIAC; doi:10.3334/CDIAC/OTG.NDP093_GLODAPv2).