I can suggest this software: “Climate change with Monthly Data” https://www.actswithscience.com/moclic222/

Also check this out for more tools: https://climatedataguide.ucar.edu/climate-data-tools-and-analysis

But if you are a programmer, you can use R and Python to perform you analyses.

These R Packages will do great help:

- ' trendchange ' ( for Innovative Trend Analysis, Sequential Mann-Kendall Analysis and Distribution free CUSUM test )

- 'TSA' available from 'http://homepage.stat.uiowa.edu/~kchan/TSA.htm'\

- You can learn more about trend analysis using R with this book: "Introductory Time Series with R" by Cowpertwait and Metcalfe.

GrADS can also be a good choice to accomplish it. You may check (GrADS) its home page for more details

http://cola.gmu.edu/grads/

Best,

Other suggestion you can use Climate data operator CDO from https://code.mpimet.mpg.de/projects/cdo, look at the user guide.

The R-tool can be another platform for trend calculation. Please check this; http://greenbrown.r-forge.r-project.org/man/Trend.html

I think you can start by using R and RStudio to work and visualize some of your time series data, addressing missing observations and running some plots if possible. R is a great tool and will improve how fast you can organize your database. Then you could start with some trend analysis, i suggest this simple tutorial to start:

http://r-statistics.co/Time-Series-Analysis-With-R.html

http://elib.sfu-kras.ru/bitstream/handle/2311/11831/s044-010.pdf?sequence=1

https://naukovedenie.ru/PDF/75TVN615.pdf

Climate indices can be calculated using Microsoft Office Excel, Microsoft Office Access, SPSS Statistics, Statistica, and a number of other programs for mathematical data processing, as well as using mathematical software packages such as MathCAD or Matlab. However, due to the large amount of data, this is quite inconvenient. Before working with such programs, you need to create a database of stations yourself, organize this data, interpret the values to exclude gaps, which takes a lot of time, and often write the appropriate program code to build regression estimates. Therefore, there is a need to create software that allows you to quickly and conveniently calculate and analyze climate indices.

Based on the processing of primary data from weather stations, various statistical characteristics of the climate are obtained, which are subsequently used for forecasting. In practice, several types of values are usually used for various climate parameters: average values, extreme (highest and lowest) values, and amplitudes.ке обычно используют несколько типов значений для различных климатических параметров: средние значения, экстремальные (наибольшие и наименьшие) значения и амплитуды.

The easiest will be to use CDO. Other software/languages like R, GraDs, python are also good.

I use R, but if you want very good graphical output R is not good at all.

Riflan Mohamed

I suggest R along with R -Studio.

Use 'modifiedmk' and 'trendchange' packages. I attached tutorial document and videos for your reference.

Follow the tutorial mentioned here... also use the google videos

https://www.youtube.com/watch?v=lfGIdtr8oPA&t=92s

https://www.youtube.com/watch?v=uh4-uOJ7lB0&t=413s

The best option would be Matlab (2019/2020 v) or NCL

R has quite a few amazing packages to explore, carry out trend analysis and statistics.

Some popular analysis like five years moving average or using Mann- Kendall test can be very easily performed using 'Kendall' and 'trend' packages

Data MAKESENS 2 0 beta

link:"https://www.researchgate.net/publication/259581072_MAKESENS_2_0_beta"

You can make analysis with Innovative Polygon Trend Analysis Method which the newest trend analysis method of Zekai Şen.

Chronostat can also do a great job

in addition to Gokmen Ceribasi comment, you can use 'trendchange' package in R to perform innovative trend analysis

Hello, in order to evaluate climate change by software, it is necessary to have spatial and temporal information and to study the trend of seasonal changes for a large region of the world.

In my opinion R is good.

I recommend R Studio for easy and prompt trend analysis.

XLSTAT

Hello big boy

For the analysis of climate trends you can use several software programs in fact. For example, I used Excel to see the trend and breaks in the climate series and I also used STATA for comparison purposes. There are others that use INSTAT and R but I haven't actually used either of these.

I have posted the following answer in several other questions related to trend analysis. For the sake of new visitors, I am posting the following again:

I have developed two R-Packages namely 'modifiedmk' and 'trendchange' to perform various trend tests.

In case if the timeseries is not affected by Auto-Correlation (also called as - Serial Correlation), I would suggest using:

· Non-Parametric Mann-Kendall Trend Test (mkttest) from modifiedmk package

· Non-Parametric Spearman’s Rank Correlation Trend Test (spear) from modifiedmk package

· Innovative Trend Analysis (innovtrend) from trendchange package

· Innovative Polygon Trend Analysis (ipta) from trendchange package

If the series is affected by serial correlation, I would suggest using an ensemble of methods instead of using just one modified version of non-parametric test from ‘modifiedmk’ package:

· Pre-Whitening with Mann-Kendall Test (pwmk)

· Trend Free Pre-Whitening with Mann-Kendall Test (tfpwmk)

· Bias-Corrected Pre-Whitening with Mann-Kendall Test (bcpw)

· Bootstrapped Mann-Kendall Trend Test with Optional Bias Corrected Prewhitening (pbmk)

· Block Bootstrapped Mann-Kendall Trend Test (bbsmk)

· Block Bootstrapped Spearman’s Rank Correlation Trend Test (bbssr)

· Modified Mann-Kendall Test Using the Hamed and Rao (1998) Variance Correction Approach (mmkh)

· Modified Mann-Kendall Test Using the Yue and Wang (2004) Variance Correction Approach (mmky)

It is also important to indicate the trend change points in the time series (When the changes in the trend occurred) along with the trend results. Trend Change Points can be analysed using the following functions from ‘trendchange’ package:

· Distribution Free CUSUM Test (dfcusum)

· Sequential Mann-Kendall Test (sqmk)

M-K Test

Sen's Slope Estimator

M M K Test

Prewhitney Test

ITA Method

The above tests you can do using XLSTAT software.

You can also carry out the same using R-studio software.