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Started 23 August 2024

Why have changes in the North Atlantic Oscillation increased during the 20th century? Can climate change be predicted in the future?

Why have changes in the North Atlantic Oscillation increased during the 20th century? Can climate change be predicted in the future?
The North Atlantic Oscillation explains a large part of the climate variability across the North Atlantic Ocean From the east coast of North America across Europe, many studies of the North Atlantic Oscillation in extreme weather conditions in this region, especially in Winter is relevant. It has motivated a significant study of this pattern. However, an overlooked feature is how the North Atlantic Oscillation has changed over time. There is a significant increase in the variance of the pattern. The North Atlantic Oscillation (NAO) increased during the 20th century from 32% in 1930 to 53% at the end of the 20th century. Whether this change is due to natural variation, a forced response to climate change, or a combination thereof is not yet clear. However, we found no evidence for a forced response from the Model Comparison Project Phase 6 (CMIP6) set of 50 pairwise models. All of these models showed significant internal variability in the strength of the North Atlantic Oscillation, but were biased toward it. In the region, this has direct implications for both long-term and short-term forecasting where regional climate changes are extreme. The North Atlantic Oscillation (NAO) is a pattern of variability associated with sea surface pressure over the North Atlantic Ocean with a subpolar low and subtropical high. The NAO is associated with large-scale changes in the position and intensity of both the storm track and the jet stream over the North Atlantic, and therefore plays a direct role in shaping the atmospheric transport of heat and moisture across the basin (Fasullo et al., 2020). ). It has also been shown that the NAO has a large effect on the Atlantic meridional overturning circulation and therefore the oceanic heat transfer, and this is the largest time scale of 20-30 years, which leads to changes in northern hemisphere temperatures of a few tenths. a degree (Delworth and Zeng, 2016). NAO has positive and negative. It shows significant interannual phase and changes. The positive phase of NAO shows between the two phases of pressure below the normal limit in the subpolar region and high pressure above the normal limit in the subtropics. It is often associated with a decrease in temperature and precipitation, an anomaly in southern Europe and an increase in precipitation, an anomaly in northern Europe, the effects of the NAO across the basin and the positive phase are also associated with it. Positive temperature anomaly in the eastern United States. The opposite pattern and its effects are observed during the period when the NAO is in its negative phase (Weisheimer et al., (2017). It has long been established that the NAO dominates climate variability over a large part of the Northern Hemisphere. The eastern coast of North America across Europe to the center of Russia and from the Arctic in the north to the subtropical Atlantic Ocean (Horrell et al., 2003) is one of the important components of winter variability and is related to the frequency and intensity of weather extremes. in Europe (Hilock and Goodes, 2004; Scaife et al., 2008; Fan et al., 2016). Therefore, it is necessary to understand the scale of natural variability in the NAO, how the NAO responds to changes in external forcing, and whether these If current climate models fail to account for natural variability or NAO forcing, this could lead to radical predictions of extreme climate change in Europe on time scales of decades to centuries.An index for the NAO is often identified in one of two
ways. The first approach is to calculate the normalized difference in surface pressure between the subtropical high (Azores High) and subpolar low (Icelandic Low) over the North Atlantic sector. The second approach is to perform an Empirical orthogonal function (EOF) analysis on sea level pressure over the North Atlantic region. An EOF analysis separates the variability in the sea level pressure into orthogonal modes, with the first mode containing the largest proportion of the variability and each subsequent mode containing progressively less. When an EOF analysis is used to calculate the NAO, the first mode indicates the NAO index, while the second and third modes usually provide the North Atlantic ridge and Scandinavian blocking patterns (Cassou et al., 2004).

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This happened because of global warming. I that future climate changes can be prebelievedicted if the extent of global warming is tracked, as it is considered one of the most important causes of climate change. Prediction can depend on
Comparing the climate factors of this region with each other during different time periods, then using statistics to predict its shape in the future.

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How precipitation is predicted and how to use satellites and gauges such as Climate Prediction Formation (CPC), Global Precipitation Mapping Satellite
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  • Abbas KashaniAbbas Kashani
How is precipitation predicted and how can we use satellites and gauges such as Climate Prediction (CPC) Formation Algorithm (CMORPH), Global Precipitation Mapping Satellite (GSMaP), Tropical Precipitation Measuring Mission (TRMM), and some other satellites; Precipitation analysis (TMPA) used?
Precipitation is one of the important components of the global water cycle and is related to atmospheric circulation In climate and climate change that is used for weather forecasting, hydrological process modeling, disaster monitoring, etc. Because precipitation varies widely in space and time, it is accurate and reliable. Higher temporal and spatial precipitation products are needed for stakeholder decision-making. Local-scale decision-making is needed. Precipitation data can be both temporally and spatially. presented a series of spatial and temporal events indicating the tendency to increase rainfall in a particular region and the spatio-temporal distribution directly affecting the availability Water sources in rivers or watersheds. This availability of precipitation data is an important part of hydrological analysis, however, its inclusion is often insufficient and incomplete due to several factors such as the lack of both spatial and temporal observation data. Precipitation time series data, uneven number Precipitation stations, a limited number of observers and system observations, as well as manual data entry. this is . It is also difficult to obtain surface precipitation in real time. Observational data, which require preliminary investigation before they can be used directly. However, there is a need for accurate spatio-temporal and long-term precipitation data in climate change prediction, simulation study, hydrological forecasting, floods, landslides, droughts, disasters. Management and investigation of water resources Several factors that contribute to uncertainty, such as observation errors, boundary errors or initial conditions, model or system errors, scale differences and unknown parameter heterogeneity, have a significant impact on mass and distributed hydrological performance. models. Usually, the highest amount of precipitation is considered. An important meteorological input in hydrological and water quality studies is accurate measurement of precipitation. For reliable and consistent hydrological forecasts, the quantity and quality of water, the accuracy of precipitation data, is needed. including intensity, duration, geographic patterns and extent, significant impact on land surface output and hydrological models. has it. Large-scale hydrological models often rely on remotely sensed precipitation data from satellite sensors due to the lack of ground-sensing equipment and rain gauge networks. Gauges or satellites show regional and temporal variability and measurement errors although ground sensor Networks such as rain gauges and radars provide the most amount. Direct observations of surface precipitation and frequent They provide measurements with high time frequency The systems have significant drawbacks. Gauges limited to Point-scale observations, but they are also susceptible Misleading readings due to wind and evaporation effects. In addition, spatial interpolation of point-based observations Adds uncertainty to the final grid in addition to measurement errors Spatial precipitation dataset The distribution and density of gauges are critical factors Adequacy measurement has been shown by several studies to be fragmented And irregular rain gauge networks have a significant impact It can be based on the uncertainty of the hydrological model and that uncertainty It decreases with increasing densitometer or optimization distribution pattern. ground radar On the other hand, networks often provide continuous Spatial coverage with high spatial and temporal resolution. However, their accuracy is affected by signal attenuation and Extinction, surface scattering, illumination and effects, and Uncertainty in reflectivity-rainfall-rate relationship The latest technologies, such as remote sensing technology, It can overcome the lack or unavailability of precipitation data In the previous period, this means through satellite The possibility of obtaining precipitation data remotely measurements, thereby simplifying the collection process At any time and from any region, satellites generally have several Advantages over surface observation rain stations The measurement of precipitation amounts is one of the above spatial and temporal resolution with a wide coverage area, Near real-time data, continuous recording, quick access, weather effects, less field variability and easy data collection Because of the free download now, there are several satellite-based precipitation products available, each of which is different. Degrees of accuracy of the Climate Prediction Center (CPC) Formation Algorithm (CMORPH), Global Precipitation Mapping Satellite (GSMaP), Tropical Precipitation Measuring Mission (TRMM),Multisatellite Precipitation Analysis (TMPA) and others.
Abbas Kashani added a reply
How precipitation is predicted and how to use satellites and gauges such as Climate Prediction Formation (CPC), Global Precipitation Mapping Satellite (GSMaP), Tropical Rainfall Measuring Mission (TRMM) and some other satellites. Precipitation analysis (TMPA) is used?
Precipitation is one of the important components of the global water cycle and is related to atmospheric circulation. in climate and climate change used for weather forecasting, hydrological process modeling, disaster monitoring, etc. Because precipitation varies greatly in space and time, it is accurate and reliable. Higher spatial and temporal precipitation products are needed for stakeholder decision making. Local decision-making is needed. Precipitation data can be both temporal and spatial. It presented a set of spatial and temporal events that indicate the tendency for rainfall to increase in a particular region and the spatio-temporal distribution that directly affects availability. Water sources in rivers or watersheds. The availability of precipitation data is an important part of hydrological analysis, however, its inclusion is often insufficient and incomplete due to several factors such as the lack of spatial and temporal observation data. Precipitation time series data, uneven number of precipitation stations, limited number of observers and system observations, as well as manual data entry. it is . It is also difficult to obtain surface precipitation in real time. Observational data that require preliminary investigation before direct use. However, in climate change prediction, simulation study, hydrological forecasting, floods, landslides, droughts, disasters, there is a need for accurate spatio-temporal and long-term precipitation data.
Management and investigation of water resources. Several factors that contribute to uncertainty, such as observation errors, boundary errors or initial conditions, model or system errors, scale differences and heterogeneity of unknown parameters, have a significant impact on mass and scattered hydrological performance. models. It is usually considered to be the highest amount of rainfall. An important meteorological input in hydrological and water quality studies is the accurate measurement of precipitation. For reliable and consistent hydrological forecasts, water quantity and quality, the accuracy of precipitation data is required. including intensity, duration, geographic patterns and extent, significant impact on land surface output and hydrological models. has it. Large-scale hydrological models often rely on remotely sensed precipitation data from satellite sensors due to the lack of ground sensing equipment and rain gauge networks. Gauges or satellites show regional and temporal changes and measurement errors even though the ground sensor
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