Do I really need PCA or scaling in this case? My dataset have 141 variables, all are numeric. To do clustering based on them, it seems that PCA is required to reduce dimensionality.
The var plot shows that variance among these variables are unstable. ( variance of all variables are under 0.1)
I scaled the dataset, and the var plot shows stability.
 
Then I do PCA on it and make a scree plot to show the percentage of variance in each components. I was shock that first 10 components only takes up 20% of the total variance (The chosen factors should explain 70 to 80% of variance at least).
I tried PCA without scaling, and still find that first 10 components explain less than 50% of variance.

Does it mean that the PCA is not required, even if the number of variables are large? And do I need to scale the data?
 A: There is no rule that says "thou must scale your data with PCA".
There are plenty of cases where scaling is bad, for example with you have latitude and longitude.
Scaling is often better than not scaling if the axes are very different. PCA is often helpful if you have linear correlations and want to get rid of this redundancy in the data. If you want to retain this redundancy (because it helps solving your problem), PCA can even be harmful!
So whenever you have doubt, don't blindly apply any such transformation, but rather try to understand your problem better. What the right approach is depends on your data and objective, not on some textbook heuristic such as using PCA.
In your plot it does appear as if you have some near-duplicate variables. You may want to first merge these pairs. Then check for further highly correlated variables. Because these harm PCA, usually the result gets much more meaningful when you have eliminated the obvious relationships. Also get rid of any clearly useless variables.
