Temperature in Papua New Guinea increased to 24.83 celsius in 2024 from 24.77 celsius in 2023. This dataset includes a chart with historical data for Papua New Guinea Average Temperature.

Papua New Guinea Heat Index data was reported at 0.900 Day in 2020. This records an increase from the previous number of 0.180 Day for 2019. Papua New Guinea Heat Index data is updated yearly, averaging 0.020 Day from Dec 1970 (Median) to 2020, with 51 observations. The data reached an all-time high of 1.190 Day in 2018 and a record low of 0.000 Day in 1996. Papua New Guinea Heat Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Papua New Guinea – Table PG.World Bank.WDI: Environmental: Climate Risk. Total count of days per year where the daily mean Heat Index rose above 35°C. A Heat Index is a measure of how hot it feels once humidity is factored in with air temperature.;World Bank, Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org;;

This data set contains two ASCII files (.txt format), one providing net primary production (NPP) component data for a lower montane rainforest and the other providing climate data. The NPP studies were conducted at Marafunga (6.00 S 145.18 E) in the highlands of Papua New Guinea to the east of Mount Kerigomna, about 25 km west of the town of Goroka. LAI, litterfall, litter standing crop and decomposition, and nutrient content of different vegetation components were measured from November 1970 through December 1971 at four representative forest stands: Ridge Top; Ridge Gap; Valley; and Slope. Forest inventories and field measurements of above- and below-ground biomass were made by destructive harvest at a fifth stand (Ridge Top) during October-December 1970 and April-August 1971. The results of these studies are given for the forest at large. The only component of NPP determined at Marafunga was litterfall (755 g/m2/year).

The climate data in this data set are available from a weather station at the Marafunga sawmill, about 2.5 km from the Marafunga study sites, and from a weather station in a clearing in the primary forest. A rainfall record for the period 1969-1971 was made daily at the sawmill. Records of maximum and minimum temperatures were made every two weeks for the period December 1970-August 1971 at litter level at the four nondestructive study sites.

The O&A Underway data set includes data collected during the voyages of Australia's Marine National Facility and CSIRO marine research vessels. Underway data typically consists of voyage track point data that has been interpolated into a standard time interval.

The subset extracted for MARVL contains data on the continental shelf as defined by the 200 metre depth contour from the 2012 Bathymetric dataset merged (spatial union) with the Exclusive Economic Zone (EEZ) in the Australian Maritime Boundary dataset (http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_63565) available from Geosciences Australia (GA). This subset of the data contains 7,179,357 position records from 206 voyages and includes air/water temperature, pressure and salinity (derived) and spans from 1995 to the present.

The full data set is held in the O&A Information & Datacentre Data Warehouse, which currently holds over 28,457,501 position records from 313 voyages collected since 1986. This data includes air pressure, air and sea surface temperature, water depth, humidity, fluorescence, pyranometer, wind, par, ship heading and speed, rain, radiometer and salinity (derived). Individual metadata records have been created for each research voyage.

This dataset consists of rasters representing downscaled climate change scenarios (8 km resolution) for the Torres Strait and Papua New Guinea regions for 1990, 2055, 2090. This includes estimated mean surface relative humidity (%), wind speed, rainfall rate (mm per day) and surface temperature (degrees Celsius) estimated from simulated conditions for 1980?1999, 2046-2065 and 2080?2099 time periods. Also included is the relative change of each attribute with respect to 1990.

For the past decade the Conformal Cubic Atmospheric Model (CCAM) has been the mainstay of CSIRO dynamical downscaling (McGregor 1996, 2005a, 2005b; McGregor and Dix 2001, 2008). CCAM is an atmospheric GCM formulated on the conformal-cubic grid. CCAM includes a fairly comprehensive set of physical parameterizations. The GFDL parameterizations for long-wave and short-wave radiation (Schwarzkopf and Fels 1991; Lacis and Hansen 1974) are employed, with interactive cloud distributions determined by the liquid and ice-water scheme of Rotstayn (1997). The model employs a stability-dependent boundary layer scheme based on Monin-Obukhov similarity theory (McGregor et al. 1993), together with the non-local treatment of Holtslag and Boville (1993). A canopy scheme is included, as described by Kowalczyk et al. (1994), having six layers for soil temperatures, six layers for soil moisture (solving Richard's equation) and three layers for snow. The cumulus convection scheme uses a mass-flux closure, as described by McGregor (2003), and includes downdrafts, entrainment and detrainment. CCAM is not only used for climate studies (Nguyen et al. 2011), it is also used in a short-range weather forecast system (Landman et al. 2012).

Methods:

All primary simulations were completed using CSIRO’s global stretched-grid, Conformal Cubic Atmospheric Model (CCAM; McGregor and Dix, 2008) run at 60 km horizontal resolution over the entire globe, while further downscaling to 8 km was conducted for selected partner countries. The CCAM model was chosen for the downscaling because it is a global atmospheric model, so it was possible to bias-adjust the sea-surface temperature in order to improve upon large-scale circulation patterns. In addition, the use of a stretched grid eliminates the problems caused by lateral boundary conditions in limited-area models. The model has been well tested in various model inter-comparisons and in downscaling projects over the Australasian region (Corney et al., 2010). CCAM 60 km Global simulations: These simulations were performed for six host global climate models (CSIRO?Mk3.5, ECHAM/MPI?OM, GFDL-CM2.0, GFDL?CM2.1, MIROC3.2 (medres) and UKMO?HadCM3) that were deemed to have acceptable skill in simulating the climate of the Pacific Climate Change Science Program region. The period 1961-2099 was simulated for the A2 (high) emissions scenario only.

In these simulations, the sea-surface temperature bias?adjustment was calculated by computing the monthly average biases of the global models for the 1971-2000 period, relative to the observed climatology, based upon the method of Reynolds (1988). These monthly biases were then subtracted from the global climate model monthly sea-surface temperature output throughout the simulation. This approach preserves the inter- and intra-annual variability and the climate change signal of the host global climate models.

CCAM 8 km Global simulations: Due to computational cost, only three of the CCAM 60 km global simulations (those using SSTs from GFDL-CM2.1, UKMO-HadCM3 and ECHAM5) were selected for further downscaling to 8 km. Of the six host models, these three GCM simulations showed a low, middle and high amount of global warming into the future, respectively. A scale-selective digital filter developed by Thatcher and McGregor (2009) was used to impose the broad-scale (scales greater than approximately 500 km) fields of temperature, moisture and winds above pressure-sigma level .9 (about 1 km above the surface) from the 60 km simulations onto the 8 km simulations.

Further detail about the methods used in the development of this dataset is provided in: Katzfey, J., Rochester, W., (2012) Downscaled Climate Projections for the Torres Strait Region: 8 km2 results for 2055 and 2090, NERP TE Milestone Report, available: http://nerptropical.edu.au/Project11.1MilestoneReport%E2%80%93May2012%E2%80%93DownscaledClimate

Limitations:

Climate change projections are inherently uncertain. The future climate will be determined by a combination of factors, including levels of greenhouse gas emissions, unexpected events (e.g. volcanic eruptions), changes in technology and energy use, and sensitivity of the climate system to greenhouse gases, as well as natural variability. Exactly how these factors will unfold is unknown. Climate models have different internal dynamics and parameterisations, and thus respond somewhat differently to the same inputs, producing a range of possible futures. This concern is partly addressed in the current study by selecting CMIP3 GCMs that reproduce current climate reasonably well, then using techniques for bias correction of SSTs that improve their representation in the current climate, but preserve the projected climate change signal and the internal variability. In addition, multi-model means of variables such as temperature and rainfall are assessed to capture the most plausible possible futures. However, the full range of future climate as projected by all GCMs should be considered as well. The best solution is to pick three cases for a given application: the worse case, the best case and the most representative (most evidence) case.

This research has revealed some new insights into the potential future climate in Torres Strait, given our current understanding. In assessing the impact of these projections, careful analysis is required. The results presented from this research are only the first step in developing a greater understanding of future climate in Torres Strait.

Format:

This dataset consists of 5 rasters (in netcdf format) for each attribute (temperature, wind speed, rainfall rate and relative humidity) consisting of 3 time periods (1990, 2055, 2090) plus relative change (1990 to 2055 and 1990 to 2090) for a total of 20 rasters files.

References:

• Corney SP, Katzfey JF, McGregor JL, Grose MR, White CJ et al (2010) Climate futures for Tasmania: climate modelling technical report. Antarctic Climate and Ecosystems Cooperative Research Centre, Hobart
• Katzfey JJ, McGregor JL, Nguyen KC and Thatcher M (2009) Dynamical downscaling techniques: Impacts on regional climate change signals. In MODSIM09 Int. Congress on Modelling and Simulation, www.mssanz.org.au/modsim09 13:2377-2383
• McGregor JL (2005) C-CAM: Geometric aspects and dynamical formulation. CSIRO Atmospheric Research Technical Paper 43
• McGregor JL and Dix MR (2008) An updated description of the Conformal-Cubic Atmospheric Model. In: “High Resolution Simulation of the Atmosphere and Ocean”, Hamilton K and Ohfuchi W (Eds), Springer, 51–76
• Nguyen KC, Katzfey JJ, McGregor JL (2011) Global 60 km simulations with CCAM: evaluation over the tropics. Clim Dyn online first. doi:10.?1007/?s00382-011-1197-8
• Reynolds RW, Smith TM, Liu C, Chelton DB, Casey KS and Schlax MG (2007) Daily high-resolution blended analyses for sea surface temperature. J Climate 20:5473-5496